Vol.:(0123456789)
Sports Medicine (2022) 52:257–286 
https://doi.org/10.1007/s40279-021-01552-4
SYSTEMATIC REVIEW
The Training of Medium‑ to Long‑Distance Sprint Performance 
in Football Code Athletes: A Systematic Review and Meta‑analysis
Ben Nicholson1 · Alex Dinsdale1 · Ben Jones1,2,3,4,5 · Kevin Till1,2
Accepted: 24 August 2021 / Published online: 9 September 2021 
© The Author(s) 2021
Abstract
Background Within the football codes, medium-distance (i.e., > 20 m and ≤ 40 m) and long-distance (i.e., > 40 m) sprint 
performance and maximum velocity sprinting are important capacities for success. Despite this, no research has identified 
the most effective training methods for enhancing medium- to long-distance sprint outcomes.
Objectives This systematic review with meta-analysis aimed to (1) analyse the ability of different methods to enhance 
medium- to long-distance sprint performance outcomes (0–30 m, 0 to > 30 m, and the maximum sprinting velocity phase 
[Vmax]) within football code athletes and (2) identify how moderator variables (i.e., football code, sex, age, playing standard, 
phase of season) affected the training response.
Methods We conducted a systematic search of electronic databases and performed a random-effects meta-analysis (within-
group changes and pairwise between-group differences) to establish standardised mean differences (SMDs) with 95% con-
fidence intervals and 95% prediction intervals. This identified the magnitude and direction of the individual training effects 
of intervention subgroups (sport only; primary, secondary, tertiary, and combined training methods) on medium- to long-
distance sprint performance while considering moderator variables.
Results In total, 60 studies met the inclusion criteria (26 with a sport-only control group), totalling 111 intervention groups 
and 1500 athletes. The within-group changes design reported significant performance improvements (small–moderate) 
between pre- and post-training for the combined, secondary (0–30 and 0 to > 30 m), and tertiary training methods (0–30 m). 
A significant moderate improvement was found in the Vmax phase performance only for tertiary training methods, with no 
significant effect found for sport only or primary training methods. The pairwise between-group differences design (experi-
mental vs. control) reported favourable performance improvements (large SMD) for the combined (0 to > 30 m), primary 
(Vmax phase), secondary (0–30 m), and tertiary methods (all outcomes) when compared with the sport-only control groups. 
Subgroup analysis showed that the significant differences between the meta-analysis designs consistently demonstrated a 
larger effect in the pairwise between-group differences than the within-group change. No individual training mode was found 
to be the most effective. Subgroup analysis identified that football code, age, and phase of season moderated the overall 
magnitude of training effects.
Conclusions This review provides the first systematic review and meta-analysis of all sprint performance development meth-
ods exclusively in football code athletes. Secondary, tertiary, and combined training methods appeared to improve medium-
long sprint performance of football code athletes. Tertiary training methods should be implemented to enhance Vmax phase 
performance. Nether sport-only nor primary training methods appeared to enhance medium to long sprint performance. 
Performance changes may be attributed to either adaptations specific to the acceleration or Vmax phases, or both, but not 
exclusively Vmax. Regardless of the population characteristics, sprint performance can be enhanced by increasing either the 
magnitude or the orientation of force an athlete can generate in the sprinting action, or both.
Trial Registration OSF registration https:// osf. io/ kshqn/.
Extended author information available on the last page of the article
258
 
B. Nicholson et al.
Key Points 
Research evaluating the medium- to long-distance sprint 
performance in the football codes is biased towards male 
soccer athletes involved in tertiary training methods 
(e.g., strength, power, and plyometrics training).
Medium- to long-distance sprint performance of football 
code athletes can be enhanced through secondary (i.e., 
resisted or assisted sprinting), combined (i.e., primary or 
secondary and tertiary methods) (0–30 and 0–>30 m), 
and tertiary training methods (0–30 m). Tertiary training 
methods were the only mode to significantly enhance 
the maximum velocity phase performance. However, 
sport-only training or primary training methods did not 
enhance performance. Despite the use of performance 
outcomes >20 m as a proxy measure of maximum veloc-
ity performance, performance changes may be attributed 
to either or both adaptations specific to the acceleration 
or maximum velocity phases, not exclusively maximum 
velocity.
Independent of the population characteristics, findings 
suggest that practitioners should develop either the 
magnitude or the orientation of forces, or both, that an 
athlete can generate and express in the sprinting action to 
improve medium- to long-distance sprint performance.
1 Introduction
Football athletes are defined as those who are competing 
within a football code. These typically include soccer, 
American football, Canadian football, Australian football, 
rugby union, rugby league, rugby sevens, Gaelic football, 
and futsal. Football code athletes should be proficient 
at sprinting both short (i.e., 5–20 m) and medium–long 
(> 20 m) distances [1–5]. Although less frequent, play-
ers also perform medium- (i.e., > 20 and ≤ 40 m) to long-
distance sprints (e.g., > 40 m), enabling athletes to express 
maximum sprinting velocity (Vmax) capabilities, particularly 
from moving starts [4, 6–14]. Very large associations have 
been demonstrated between Vmax and sprint performance 
(0–36.6 m, r = 0.94; 18.3–36.6 m, r = 0.97) in football code 
athletes, whereas the relative rate of acceleration remained 
the same irrespective of sprinting performance, indicating 
that a higher Vmax enables a superior acceleration perfor-
mance [8]. Given that most athletes accelerate in a similar 
manner relative to Vmax, it may be that Vmax serves as the 
upper threshold or limiting factor in the acceleration phase 
performance. Therefore, improving an athlete’s sprinting 
Vmax may indirectly improve acceleration [8]. Hence, the 
development of Vmax and medium–long sprint performance 
is a vital component of athletic performance within the foot-
ball codes [15–18].
Sprint performance over distances greater than 20 m (i.e., 
0–30 and 0–40 m split time or velocity) has been shown to 
be a differentiating factor between playing standards [19–21] 
and age categories [19, 21, 22] and is associated with suc-
cess in key attacking and defensive performance indicators in 
football code athletes (e.g., rugby sevens [16], rugby league 
[17, 18], soccer [23]). This body of evidence emphasises the 
importance of sprint performance for football performance 
and player development. Unlike sprinters or non-athletic 
populations, sprint performance development programmes 
in football code athletes are typically performed concur-
rently with multiple other potentially contrasting physical 
capacities (e.g., endurance) alongside the code’s specific 
technical–tactical skills. Therefore, developing sprint per-
formance is a challenge for all practitioners involved in the 
football codes [15, 19, 24]. The review by Nicholson et al. 
[25] reported that short-sprint performance outcomes (0–5, 
0–10, and 0–20 m) were enhanced concurrently with code-
specific training in football code athletes, but no research has 
identified the most effective training methods for enhancing 
medium- to long-distance sprint outcomes in football code 
athletes (e.g., 0–30, 0–40, 0–50 m). This highlights the need 
for specifically targeted sprint-based research to understand 
the most effective, evidence-based methods for developing 
sprint performance over medium to long sprint distances 
(e.g., 30–50 m).
Sprinting is a multidimensional skill with distinct phases 
(e.g., acceleration and Vmax). The sequential phases present 
shifting kinetic and kinematic outcomes as running velocity 
increases [26]. The kinetic changes include a reduction in 
the relative contribution of horizontal and increasing con-
tribution of vertical ground reaction forces [26]. Kinematic 
outcomes include progressively greater stride length and fre-
quency, reduced contact times, and the trunk lean becoming 
closer to vertical [26]. As a population, football code ath-
letes exhibit different physical and technical approaches to 
sprinting [27, 28] when compared with well-trained sprint-
ers. Notably, Vmax is achieved at shorter distances (e.g., 
15–40 vs. 40–60 m, respectively) with a lower Vmax (~ 7–10 
vs. > 12 m·s−1) compared with well-trained elite male sprint-
ers [8, 9, 27, 29–31]. Furthermore, a higher Vmax percent-
age is attained at shorter distances (e.g., 90% at 13.7 m in 
American football [8]; 96% at 21 m in rugby [9]). This high-
lights the need for specifically targeted sprint-based research 
within this population.
259
Training Medium to Long Sprint Performance in Football Athletes
Previous reviews of the literature and meta-analyses 
[32, 33] assessing mixed population cohorts (i.e., sprint-
ers, team sport, and non-athletic populations) and several 
training studies evaluating the effectiveness of sprint training 
interventions [34–36] reported that sprint performance is a 
trainable capacity. However, the responses to sprint develop-
ment were reported to be highly variable [32, 34, 37, 38]. 
Training effects appear to be mode specific, with distance-
specific performance changes (e.g., 0–30 and 0 to > 30 m) 
associated with phase-specific adaptations (i.e., accelera-
tion vs. Vmax [32, 33]). Training modes are typically classi-
fied based on task specificity into the following subgroups: 
primary (e.g., sprint technique, sprinting), secondary (e.g., 
resisted or assisted sprinting), or tertiary (e.g., non-specific 
methods, including resistance training and plyometrics) [39]. 
Limitations in the literature mean that the best method of 
enhancing medium to long sprint performance, both indi-
vidually and across football codes, is currently unclear. 
These limitations include (1) a lack of reviews exclusively 
including football code athletes, instead including sprint-
ers and non-athletes [32, 33, 40–49]; (2) a lack of studies 
examining all training modalities across football code ath-
letes [32, 33, 40–49]; and (3) previous systematic reviews 
and meta-analyses [32, 33, 41] have misclassified training 
modes by failing to account for the normal training practices 
undertaken by training intervention groups (e.g., training 
categorised as a resisted sled intervention also including 
two strength sessions per week). These limitations heavily 
influence the interpretation and knowledge associated with 
sprint training interventions for applying evidence-based 
practices within football code athletes. Hence, the effec-
tive development of medium to long sprint performance is 
a collective problem across codes. A cross-football codes 
systematic review would provide a more comprehensive 
overview of the available literature than one focusing on 
an individual sport, while also comparing best methods of 
developing medium to long sprint performance. However, 
the magnitude and direction of the training response may be 
affected by ‘moderator’ variables, presenting changes based 
on population characteristics such as the sport [50], age [42], 
and sex [51] of the athlete and on training phase (e.g., pre-
season [33]). Therefore, it is important to also identify the 
moderator variables and evaluate the extent that they may 
affect the resultant training effect [52].
This systematic review and meta-analysis aimed to (1) 
analyse the impact of different methods to enhance medium- 
to long-distance sprint performance outcomes (0–30 m, 0 
to > 30 m, and the Vmax phase) within football code athletes 
and (2) identify how moderator variables (i.e., football code, 
sex, age, playing standard, phase of season) affect the train-
ing response.
2  Methods
2.1  Design and Search Strategy
A systematic review and meta-analysis was conducted in 
accordance with the Preferred Reporting Items for System-
atic Reviews and Meta-Analyses (PRISMA) statement [53] 
and followed the PROSPERO guidelines. Given the nature 
of the project, the review protocol was prospectively regis-
tered on the database for Open Science Framework (OSF: 
https:// osf. io/ kshqn/). A systematic search of electronic data-
bases (PubMed, The Cochrane Library, MEDLINE, SPORT-
Discus, and CINAHL, via EBSCOhost) was conducted to 
identify original research articles published from the earli-
est available records up to and including 4 December 2019. 
Boolean search phrases were used to include search terms 
relevant to football code athletes (population), the training 
intervention (dependent variable), and the sprint perfor-
mance outcomes (independent variable). Relevant keywords 
for each search term were determined through pilot search-
ing (screening of titles/abstracts/keywords/full texts of pre-
viously known articles). Keywords were combined within 
terms using the ‘OR’ operator, and the final search phrase 
was constructed by combining the three search terms using 
the ‘AND’ operator (Table 1).
2.2  Study Selection
Duplicate records were identified and removed, and the 
remaining records were screened against the predefined 
inclusion and exclusion criteria (Table 2). Studies were 
Table 1  Database literature 
search strategy
Search term
Keywords
1. Sports population
“soccer” OR “football” OR “rugby” OR “futsal”
(NOT/- “sprinters” OR “swimming” OR “cycling” OR “Paralympic”)
2. Training intervention
“sprinting” OR “sprint” OR “training” OR “speed” OR “resisted” OR 
“assisted” OR “resistance” OR “power” OR “strength” OR “plyo-
metric” OR “weightlifting” OR “strongman” OR “technique” OR 
“weight” OR “sled” OR “intervention” OR “sprint mechanics”
3. Outcome measures
“sprint performance” OR “acceleration” OR “velocity”
Search phrase:
1 AND 2 AND 3
260
 
B. Nicholson et al.
screened independently by two researchers (BN, AD). The 
screening of the journal articles was completed over two 
phases. Studies were initially excluded based on the content 
of the titles and abstracts, followed by a full-text review. If 
the reviewers’ decisions differed, reviewers met to come to 
an agreed decision on the paper. Disparities in study selec-
tion were resolved by a third reviewer (KT).
2.3  Data Extraction
One author (BN) extracted the following data using a spe-
cifically designed standardised Microsoft Excel spread-
sheet: general study information (i.e., author, year), sub-
ject characteristics (i.e., sample size, sex, age, body mass, 
height, sport, training status, performance level), training 
intervention characteristics (i.e., training methods, control 
group information, number of sessions per week, duration 
of training intervention, total amount of training sessions, 
training intensity, training volume, testing distances, test-
ing equipment, training surface, other training, reported 
training-related injuries), and primary outcome measures 
(i.e., pre- and post-training intervention means and stand-
ard deviations [SDs]). All studies that included the time 
or velocity achieved from the initial start position (0 m) to 
between > 20 and ≤ 30 m and between 0 and > 30 m were 
categorised into the 0–30 m and 0 to > 30 m subgroups, 
respectively. The Vmax-phase subgroup included directly 
measured Vmax achieved or time to completion for dis-
tances > 20 m with a maximum intensity run-in distance 
of ≥ 20 m before recording time (e.g., 20–30 or 30–40 m). 
These outcomes aimed to identify distance-specific 
changes, whilst representing the longer sprint distances 
(0 to 30–50 m) performed by football code athletes and 
those commonly measured by researchers/practitioners. 
Descriptive information relating to the training activi-
ties performed in the studies was used to categorise each 
intervention into the training mode subgroups outlined in 
Table 3. If the pre- and post-outcome measure data were 
not available from the tables or the results section, the data 
were requested from the author(s). If the authors did not 
have access to these data, we extracted data on outcome 
measures from figures using WebPlotDigitizer version 
4.1 software (2018). Means and SDs/standard error of the 
mean were measured manually at the pixel level to the 
scale provided in the study’s figures.
2.4  Study Quality Assessment
The quality of the included studies was assessed using 
the same scale as in McMaster et al. [54]. This scale is 
designed to evaluate research conducted in athletic-based 
training environments from a combination of items from the 
Cochrane, Delphi, and PEDRO scales. The methodologi-
cal scale assesses the study in the following ten domains: 
inclusion criteria stated, subject assignment, intervention 
description, control groups, dependent variables definition, 
assessment methods, study duration, statistics, results sec-
tion, and conclusions. Each domain was assigned a score of 
either 0 indicating clearly no, 1 indicating maybe, or 2 indi-
cating clearly yes. The scores were then summed to assess 
the total study quality out of a maximum of 20.
Table 2  Inclusion/exclusion criteria (title/abstract screening and full screening)
Criteria
Inclusion
Exclusion
1
Studies with human subjects and a pre- and post-outcome 
measure(s) identifying sprint performance > 20 m
Studies with non-human subjects and/or no pre- and post-outcome 
measure(s) identifying sprint performance ≤ 20 m or performance 
outcomes measured using stopwatches
2
Training intervention study with the training programme clearly 
outlined, designed to produce chronic adaptations (not acute). 
Interventions including specific sprint training (resisted, 
assisted, unresisted sprinting, sprint mechanics, and technique 
training), non-specific sprint training (strength, power, plyo-
metric training, and non-traditional methods), and combined 
sprint training (combined specific, combined non-specific, and 
combined mixed methods)
Inappropriate study design: not an intervention study or an acute/
post-activation study
3
Original research article
Reviews, surveys, opinion pieces, books, periodicals, editorials
4
Population: football code athletes. Football athletes defined as 
those who are competing within a football code. Football codes 
for inclusion: soccer, American football, Canadian football, 
Australian football, rugby union, rugby league, rugby sevens, 
Gaelic football, futsal
Non-football code sports (e.g., solo, racquet/bat, or combat sports), 
match officials, or non-athletic populations
5
Healthy, able-bodied, non-injured athletes
Special populations (e.g., clinical, patients), athletes with a physi-
cal or mental disability, and athletes considered to be injured or 
returning from injury
261
Training Medium to Long Sprint Performance in Football Athletes
2.5  Data Analysis and Meta‑analyses
Data extracted from the systematic search were included in 
the meta-analyses. Improvements in sprint performance are 
typically identified by a reduction in time taken to cover a 
given distance or an increase in Vmax achieved for a given 
time point and or distance [55, 56]. Therefore, pre- and post-
time changes were reversed before conducting the analysis. 
This enabled both time and velocity changes to represent the 
same direction, thus identifying a reduction in time or an 
increase in velocity for a given distance as a positive change.
A random-effects meta-analysis was performed using 
Comprehensive Meta-Analysis Version 3.0 software (Bio-
stat, Englewood, NJ, USA) to assess the magnitude of 
change in the outcomes across the relevant primary studies 
and to explore the effect of moderator variables on the vari-
ation among study outcomes [57]. This included implement-
ing two meta-analysis approaches: (1) pre-and post-training 
within-group changes and (2) pairwise between-group effect 
difference designs. This approach provides an extensive 
review of all the available training intervention literature 
for developing sprint performance in football code athletes, 
including multiple research designs with and without sport-
only control groups. In the between-group pairwise analysis, 
for the studies with multiple intervention groups and single 
control groups [35, 36, 58–68], the control samples were 
split into two or more groups of smaller sample sizes to 
enable two or more (reasonably independent) experimental 
comparisons [69]. This aligns with our extensive design to 
evaluate all available literature without combining or remov-
ing distinct subgroups (e.g., primary and tertiary methods 
[67]). Overall summary estimates were calculated for each of 
the training type subgroups: primary, secondary, combined 
specific, tertiary, combined methods, and sport-only train-
ing (Table 3). We conducted a meta-analysis to identify the 
between-comparator group (e.g., primary vs. sport only, ter-
tiary vs. sport only) adjusted mean performance effects when 
a sport-only comparator group was available. Combining a 
within-group pre-post change design and pairwise between-
group differences enabled an evaluation of both high-quality 
controlled trial studies to evaluate training causality and to 
explore the breadth of the available literature using a range 
of research designs.
Outcome measures were converted into standardised 
mean differences (SMDs) with 95% confidence intervals 
(CI) (used as the summary statistic) and 95% prediction 
intervals (PI). The SMD represents the size of the effect of 
the intervention relative to the variability observed in that 
intervention. An inverse-variance random-effects model was 
used for the meta-analysis because it allocated a propor-
tionate weight to trials based on the size of their individual 
standard errors and facilitated analysis while controlling for 
heterogeneity across studies [70]. The inputted data included 
Table 3  Subgroup categorisation
Subgroup categories are based on previous definitions from Plisk [39] and Rumpf et al. [32]
Specific sprint training: training methods in which the athlete is simulating/performing the sprint movement pattern (see pri-
mary and secondary methods)
Tertiary methods (non-specific sprint training): training meth-
ods not involving the athlete sprinting, that have a transfer 
into sprint performance as a result of the subsequent training 
adaptations (e.g., strength, power, plyometric training). These 
may be performed individually (e.g., strength training) or 
in combination with other tertiary methods (e.g., strength, 
power, and plyometric training)
Combined specific methods: training methods that included both primary and secondary methods (e.g., sprinting + resisted sled 
sprinting)
Primary methods: training methods simulating the sprint 
movement pattern (sprint-technique drills, stride length and 
frequency exercises, and sprints of varying distances and 
intensities)
Secondary methods: training methods simulating the sprint 
action but applying overload by reducing or increasing the 
speed of the movement by applying additional resistance 
(e.g., sledges, resistance bands, weighted garments or incline 
sprints [gravity resisted]) or assistance (e.g., pulley systems, 
partner assisted or decline sprints [gravity assisted])
Combined training: training methods that included both specific sprint training (primary and or secondary methods) and tertiary methods in combination (e.g., strength, power, resisted, and 
unresisted sprint training)
Sport only training: training methods not including any specific or non-specific sprint training. This is described as a format of offensive, defensive, and match simulation technical and tactical 
drills, which may include some form of endurance training and or competitive games
262
 
B. Nicholson et al.
sample sizes, outcome measures with their respective SDs, 
and a correlation coefficient for within-subject measure-
ments. These correlation coefficients (0–30 m, r = 0.92; 0 to 
> 30 m, r = 0.92; and Vmax phase, r = 0.95) were estimated 
from prior field testing. The SMD values were interpreted 
as follows: < 0.20 as trivial, 0.20–0.39 as small, 0.40–0.80 
as moderate, and > 0.80 as large [71]. A positive SMD indi-
cated that the training intervention was associated with an 
improvement in sprint performance, whereas a negative 
SMD indicated a decrease in the respective performance 
outcome. Accompanying p values tested the null hypothesis 
that there was no statistically significant change in sprint 
performance regardless of the training method. Statistical 
significance was considered for p < 0.05. Heterogeneity 
between trials was assessed using the I2 statistic, with mod-
erate (> 50%) to high (> 75%) values used to indicate poten-
tial heterogeneity sources [72]. The I2 statistic was supported 
by reporting the Tau-squared statistic and the Chi-squared 
statistic. Sensitivity analyses were conducted for each sub-
group by repeating the analyses with each study omitted in 
turn; this examined whether any conclusions were dependent 
on a single study.
Subgroup analyses were performed to (1) compare the 
within-group change in pre- and post-sprint performance 
and pairwise between-group effects from comparative tri-
als and (2) evaluate the potential moderator variables. The 
moderator variables were determined a priori: sex (male vs. 
female), football code, playing standard (elite vs. sub-elite 
[from Swann et al. [73], the highest reported standard of 
performance]), age category (senior [mean age ≥ 18 years] 
vs. youth [mean age < 18 years]), and training phase (pre-
season vs. in-season vs. off-season).
2.6  Evaluation of Small Study Effects
Small study effects were explored through visual interpre-
tation of funnel plots of SMD versus standard errors and 
by quantifying Egger’s linear regression intercept [74] to 
evaluate potential bias. A statistically significant Egger’s 
statistic (p value < 0.05) indicated the presence of a small 
study effect.
3  Results
3.1  Overview
After duplicates were removed, 1801 studies remained. The 
study selection inclusion criteria identified 60 studies for 
inclusion in the within-group change meta-analysis and 
26/60 studies for inclusion in the pairwise between-group 
analysis (Fig. 1). The 60 studies [34–36, 58–68, 75–120] 
included multiple different research designs (with and 
without experimental control groups), providing 111 inter-
vention groups and 27 sport-only groups. Training groups 
were sub-grouped into six training classifications (sport 
only, n = 27; combined methods, n = 35; primary methods, 
n = 8; secondary methods, n = 9; tertiary methods, n = 59; 
and combined specific n = 0) to differentiate between find-
ings for distinct sprint performance outcomes (Table 3). The 
26 identified studies compared a training intervention with a 
sport-only (i.e., control) comparator group [35, 36, 58–68, 
75, 82, 88, 90, 92, 95–97, 104, 106, 113, 114, 119]. This pro-
vided 41 eligible training groups for pairwise between-group 
comparisons (sport-only training vs. combined methods, 
n = 9; primary methods, n = 3; secondary methods, n = 2; 
and tertiary methods, n = 27).
Table S1 (non-specific/tertiary, n = 59), Table S2 (com-
bined, n = 35), and Table S3 (specific, n = 17) (all in the 
electronic supplementary material [ESM]) present the indi-
vidual training group study descriptives, training interven-
tions, and sprint outcomes for the included studies. The 60 
studies [34–36, 58–68, 75–120] represented a total sample 
of 1500 football code athletes with a mean sample size of 
11.1 ± 3.9 participants per training group. In total, 56 stud-
ies were conducted in male athletes, three studies were in 
female athletes [86, 106, 116], and one was in a mixed popu-
lation [65]. The mean age of the participants included in the 
studies ranged from 11 to 26.8 years. The athlete popula-
tions ranged from sub-elite to elite [73]. Collectively, the 
training intervention durations ranged from 3 to 22 weeks 
(7.4 ± 3.1 weeks), with the intervention training frequency 
ranging between one and four sessions per week (2.1 ± 0.6) 
over 6–32 individual sessions.
Studies were conducted in soccer (n = 43), rugby league 
(n = 4), rugby union (n = 4), rugby sevens (n = 3), Ameri-
can football (n = 1), Australian football (n = 1), and mixed 
football codes (n = 4). No studies in futsal or Gaelic foot-
ball players satisfied the inclusion criteria. Studies were 
conducted in pre-season (n = 21), in-season (n = 26), or off-
season (n = 3) periods, and across pre-season and in-season 
periods (n = 2). Eight studies did not report the phase of 
the season. Sprint assessment distances ranged from 22.9 to 
50 m (0–30 m [n = 46], 0 to > 30 m [n = 20], and Vmax phase 
[n = 13]). Timing devices included electronic timing gate 
systems (n = 52), high-speed video cameras (n = 3), radar 
measurement devices (n = 2), 1080 sprint device (n = 2), a 
digital timing device (n = 1), a laser measurement device 
(n = 1), a kinematic measurement system (n = 1), and a 
mobile application (mysprint; n = 1).
Sport-only training groups were described as some format 
of offensive or defensive match simulation and technical and 
tactical drills performed over two to ten sessions per week 
across 2–6 days per week lasting between 30 and 120 min 
per session as well as some form of endurance training and 
one to two competitive or friendly games per week. Various 
263
Training Medium to Long Sprint Performance in Football Athletes
methods of endurance training were described, including 
simulated games performed in small-, medium-, or large-
sided games formats (e.g., 3 vs. 3–11 vs. 11), low-intensity 
aerobic conditioning, high-intensity interval training, and 
recreational or cardiovascular activities (e.g., basketball, bik-
ing, running, aerobics). Sport-only training was conducted 
in both pre-season and in-season periods over a duration of 
6–16 weeks.
Specific sprint-training groups completed sprinting, 
resisted and assisted sprinting, and technical sprint drills as 
individual modalities and/or in combination (e.g., complex 
and contrast sets). The training was performed 1–3 days per 
week, with intervention periods lasting from 4 to 8 weeks 
(8–21 sessions). The primary sprint-training methods 
included single-set interventions ranging from 8–10 rep-
etitions of short-distance sprints (18.3–20 m; 160–183 m 
session totals) to 4–6 repetitions of long-distance springs 
(200 m; 800–1200 m session totals). Multiple-set methods 
ranged from 2–6 sets of 2–8 repetitions of medium- to long-
distance sprints (30–50 m; 120–1200 m session totals). One 
study performed submaximal sprint efforts (85% Vmax), 
involving 4–6 sets of 4 repetitions of long sprints (50 m; 
800–1200 m session totals) [102]. Resisted sprinting was 
performed as either a single set of 3–10 repetitions of 
Fig. 1  Flow diagram of the 
process of study selection
Records idenﬁed through 
database searching
(n=5788)
Addional records idenﬁed 
through other sources
(n=34)
Records aer duplicates removed
(n=1801)
Full-text arcles assessed 
for eligibility
(n=245)
Full-text arcles excluded
(n=185)
73 sprint performance 
outcome measures ≤20m
30 Inappropriate outcome 
measure - no sprinng 
performance outcome 
30 Training programme -
not clearly outlined
16 Inappropriate 
populaon - not football 
code athletes
10 Inappropriate study 
design - irrelevant 
intervenon design
6 Full text not available
6 Inappropriate study 
design - not an 
intervenon study
7 Measured using 
stopwatches
4 Not published in English 
language
3 Inappropriate study 
design - acute/post 
acvaon study
Studies included in the 
within-group change
meta-analysis
(n=60)
Titles and abstracts 
screened
(n=1801)
Records excluded
(n=1556)
Idenﬁcaon
Screening
Eligibility
Included
Studies with a sport only
comparator group
included in the pairwise 
between-group eﬀect
meta-analysis
(n=26)
Studies excluded with no
sport only comparator 
group (n=34)
264
 
B. Nicholson et al.
short-distance sprints (18.3–20 m; 60–200 m session total) 
or multiple-set methods, ranging from 2 to 7 sets of 3–5 
repetitions of short-medium distance resisted sled sprints 
(5–40 m; 130–455 m session totals). Resisted sprint loads 
ranged from light to very heavy loads [44]. Loads were pre-
scribed based on percentage body mass (BM) (i.e., 10–80% 
BM). Assisted sprinting methods involved both single and 
multi-set methods. The single-set intervention included 1 set 
of 10 repetitions of short sprints over 18.3 m with a bungee 
cord assistive load at 14.7% BM (183 m session total [116]). 
Multi-set methods ranged from 1 to 3 sets of 3 repetitions 
of medium-distance sprints (40 m) with towing eliciting a 
0.5- to 1-s faster 0–40 m time using a sprint master towing 
device (120–360 m session total) [101]. The same study used 
a combined study arm using the same assistance load while 
also wearing a 10-lb weighted vest.
Tertiary sprint-training groups consisted of strength, 
power, and/or plyometrics training performed as individual 
modalities and/or in combination (e.g., complex and con-
trast sets). The training was performed 1–4 days per week, 
with intervention periods lasting from 4 to 22 weeks (8–32 
sessions). Lower body strength training (e.g., squat, hip 
hinge, and calf raise variations) ranged from moderate to 
supramaximal loads (55–110% one-repetition maximum 
[1RM]) with low- to high-volume training (e.g., 2–6 sets of 
2–6 repetitions and/or 2–6 sets of 8–30 repetitions). Power 
sessions consisted of ballistic (e.g., squat jump) and Olym-
pic weightlifting-type exercises (e.g., clean/snatch deriva-
tives) at low to heavy loads (15–80% 1RM to + 30% BM) 
and velocity-based training using loads corresponding to the 
mass at which optimal power is produced (1–1.1 × optimal 
power load). Volume ranged from 2 to 5 sets of 2–12 repeti-
tions. Plyometrics sessions involved low- to high-intensity 
plyometrics (e.g., ankle hops to 50 cm accentuated eccen-
tric loading drop jump at + 20% BM) for 1–12 sets of 4–20 
repetitions (20–260 foot contacts session totals). The only 
type of surface identified was a grass surface. Several of the 
sessions were performed in combination with upper body 
training.
Combined methods training groups consisted of various 
formats of both specific sprint training (primary and/or sec-
ondary methods) and tertiary methods in combination (e.g., 
strength, power, resisted and unresisted sprint training). 
These were completed as individual modalities and/or in 
combination (e.g., complex and contrast sets). The training 
was performed 1–4 days per week, with intervention periods 
lasting from 3 to 15 weeks (6–22 sessions). Strength train-
ing ranged from moderate to supramaximal loads (70–120% 
1RM) with low to high volume (e.g., 2–6 sets of 2–6 rep-
etitions and/or 3–4 sets of 8–12 repetitions). Power train-
ing consisted of ballistic (e.g., squat jump) and Olympic 
weightlifting-type exercises (e.g., clean/snatch derivatives) 
at light to heavy loads (20–86% 1RM) and/or velocity-based 
training using loads corresponding to the mass at which 
optimal power is produced (1–1.1 × optimal power load 
or 0.8–1.2 m·s−1 loads). This also included medicine ball 
throws of 3–12 kg. Volume ranges were from 2 to 6 sets of 
2–8 repetitions per set. Plyometrics sessions involved low- to 
high-intensity plyometrics (e.g., ankle hops to 75 cm hurdle 
jumps), with 2–5 sets of 1–10 repetitions (9–250 foot con-
tacts session totals). The only type of surface identified was 
a synthetic grass pitch. The specific sprint-training methods 
included single-set interventions ranging from 1 to 8 repeti-
tions of short- to long-distance sprints (5–45.72 m) or mul-
tiple-set methods, ranging from 1 to 5 sets of 3–7 repetitions 
of short- to medium-distance sprints (5–40 m; 30–800 m 
session totals) from various starting positions. Resisted 
sprint loads ranged from light to very heavy loads. Loads 
were prescribed based on absolute loads (i.e., 10–30 kg), 
percentage BM, i.e., 5–20% BM or reduction in Vmax cor-
responding to the additional resistance applied (10–60% 
reduction in Vmax). One training study used assisted sprints, 
involving 1 set of five medium-distance sprints (40 m) with 
25 m of each sprint including a 2% gradient decline (200 m 
session total [83]). Several of the sessions were performed 
in combination with upper body training.
3.2  Study Quality
The scores for the assessment of study quality [54] are 
shown in Table 4 and ranged from 11 to 20 with a mean 
score of 18 ± 1.9, demonstrating high study quality. Items 2 
(subjects assigned appropriately [random/equal baseline]), 
4 (control group inclusion), and 9 (results detailed [mea 
n ± SD, percent change, effect size]) were the most decisive 
factors in separating high-quality and low-quality studies.
3.3  Meta‑analysis
Tables S1–S3 in the ESM provide the individual study 
statistics.
3.4  Standardised Mean Difference (SMD) for 0–30 m 
Performance
For 0–30 m performance, 103 within-training group effects 
were analysed from 45 original studies [34, 36, 58–60, 
62–66, 75, 77–80, 82, 85–88, 90–100, 102–108, 113, 
115–120]. In total, 32 training and control groups from 21 
studies were eligible for a pairwise between-group analysis 
(sport-only control vs. experimental) [36, 58–60, 62–66, 75, 
82, 88, 90, 92, 95–97, 104, 106, 113, 119]. In nine studies 
[36, 58–60, 62–66], the 21 available control groups were 
split to allow comparison between the multiple training 
groups in the studies [69]. Figures 2, 3 show the SMD for 
each training type.
265
Training Medium to Long Sprint Performance in Football Athletes
Table 4  Methodological quality 
scale scores
Study
Question number
Score
1
2
3
4
5
6
7
8
9
10
Alptekin et al. [75]
2
2
2
2
2
1
2
2
0
2
17
Barr et al. [76]
2
2
2
2
2
2
2
2
2
2
20
Beato et al. [77]
2
2
2
0
2
2
2
2
2
2
18
Bianchi et al. [78]
2
2
2
0
2
2
2
2
2
2
18
Borges et al. [79]
2
2
2
0
2
2
2
2
0
2
16
Bouguezzi et al. [80]
2
2
2
0
2
2
2
2
2
2
18
Bremec [58]
2
2
2
2
2
2
2
2
2
2
20
Chelly et al. [81]
2
2
2
2
2
2
2
2
2
2
20
Christou et al. [82]
2
2
2
2
2
2
2
2
2
2
20
Cook et al. [83]
2
2
2
0
2
2
2
2
2
2
18
Coratella et al. [59]
2
2
2
2
2
2
2
2
2
2
20
Coutts et al. [84]
2
0
2
0
2
2
2
2
0
2
14
de Hoyo et al. [85]
2
2
2
0
2
2
2
2
2
2
18
Derakhti [60]
2
2
2
2
2
2
2
2
2
2
20
Douglas et al. [34]
2
2
2
2
2
2
2
2
2
2
20
Enoksen et al. [35]
2
2
2
2
2
2
2
2
2
2
20
Escobar-Álvarez et al. [86]
2
0
2
2
2
2
2
2
1
1
16
Escobar-Álvarez et al. [87]
1
0
2
0
1
2
2
2
1
0
11
Faude et al. [88]
2
2
2
2
2
2
2
2
2
2
20
Gabbett et al. [89]
2
0
2
0
2
2
2
2
1
2
15
García-Pinillos et al. [90]
2
2
2
2
2
2
2
2
0
2
18
Gil et al. [91]
2
2
2
0
2
2
2
2
2
2
18
Hammami et al. [92]
2
2
2
2
2
2
2
2
0
2
18
Hammami et al. [93]
2
2
2
0
2
2
2
2
2
2
18
Hammami et al. [61]
2
2
2
2
2
2
2
2
2
2
20
Harris et al. [94]
2
2
2
0
2
0
2
2
0
2
14
Karsten et al. [95]
2
2
2
2
2
2
2
2
0
2
18
Krommes et al. [96]
2
2
2
2
2
2
2
2
2
2
20
Lahti et al. [36]
2
2
2
2
2
2
2
2
2
2
20
López-Segovia et al. [97]
2
2
2
2
2
2
2
2
2
2
20
Loturco et al. [98]
2
2
2
0
2
2
2
2
0
2
16
Loturco et al. [99]
2
2
2
0
2
2
2
2
2
2
18
Loturco et al. [100]
2
2
2
0
2
2
2
2
0
2
16
Majdell and Alexander [101]
2
2
2
0
2
2
2
2
0
2
16
Manouras et al. [62]
2
2
2
2
2
2
2
2
0
2
18
McMaster et al. [120]
2
2
2
0
2
2
2
2
2
2
18
Meckel et al. [102]
2
2
2
0
2
2
2
2
0
2
16
Michailidis et al. [103]
2
2
2
2
2
2
2
2
0
2
18
Negra et al. [104]
2
2
2
2
2
2
2
2
0
2
18
Orange et al. [105]
2
2
2
2
2
2
2
2
2
2
20
Ozbar [106]
2
2
2
2
2
2
2
2
2
2
20
Ramírez-Campillo et al. [63]
2
2
2
2
2
2
2
2
2
2
20
Ramírez-Campillo et al. [64]
2
2
2
2
2
2
2
2
2
2
20
Ramírez-Campillo et al. [65]
2
2
2
2
2
2
2
2
2
2
20
Ramírez-Campillo et al. [66]
2
2
2
2
2
2
2
2
1
2
19
Randell et al. [107]
2
2
2
2
2
2
2
2
2
2
20
Rey et al. [108]
2
2
2
2
2
2
2
2
2
2
20
Rimmer and Sleivert [67]
2
2
2
2
2
2
2
2
0
2
18
Rønnestad et al. [68]
2
2
2
2
2
2
2
2
0
2
18
266
 
B. Nicholson et al.
Table 4  (continued)
Study
Question number
Score
1
2
3
4
5
6
7
8
9
10
Rønnestad et al. [109]
2
2
2
0
2
2
2
2
0
2
16
Ross et al. [110]
2
2
2
0
2
2
2
2
2
2
18
Scott et al. [111]
2
2
2
2
2
2
2
2
0
2
18
Shalfawi et al. [112]
2
2
2
2
2
2
2
2
2
2
20
Söhnlein et al. [113]
2
0
2
2
2
2
2
2
2
2
18
Tønnessen et al. [114]
2
2
2
2
2
2
2
2
2
2
20
Tous-Fajardo et al. [115]
2
0
2
2
2
2
2
2
0
2
16
Upton [116]
2
2
2
0
2
2
2
2
0
2
16
West et al. [117]
2
2
2
0
2
2
2
2
0
2
16
Winwood et al. [118]
2
2
2
0
2
2
2
2
0
2
16
Wong et al. [119]
2
0
2
2
2
2
2
2
0
2
16
0 = clear no, 1 = maybe, 2 = clear yes
Training type
Training
groups (n)
SMD (95% CI)
95% PI
p-
value
Sport only 
22
0.02 [-0.11, 0.15]
[-0.60, 0.64]
0.78
Combined methods
23
0.43 [0.21, 0.65]
[0.69, 1.55]
<0.001
Primary methods
6
0.20 [-0.01, 0.42]
[-0.51, 0.92]
0.06
Secondary methods
7
0.61 [0.31, 0.91]
[-0.43, 1.65]
<0.001
Ter ary methods
45
0.39 [0.24, 0.54]
[-0.63, 1.41]
<0.001
-1
-0.5
0
0.5
1
1.5
2
Heterogeneity: I2 = 92.84%; Q = 1424.88; t2 = 0.21 and df =102
← Reduced sprint performance
Increased sprint performance→
a
a
a,b
Standardised mean diﬀerence (mean ± 95% CI and 95% PI)
Fig. 2  Forest plots showing the SMD (mean [95% CI and 95% PI]) 
for the studies evaluating the between-training-group effects on 
0–30  m sprint performance. aSignificantly different to sport-only 
training, p < 0.05; bSignificantly different to primary training meth-
ods, p < 0.05. Bold formatting indicates p < 0.05. CI confidence inter-
val, PI prediction interval, SMD standardised mean difference
Training type
Training
groups (n)
SMD (95% CI)
95% PI
p-
value
Combined methods
5
0.58 [-0.93, 2.10]
[-5.20, 6.37]
0.45
Primary methods
2
0.33 [-0.58, 1.25]
N/A
0.48
Secondary methods
2
2.78 [1.53, 4.03]
N/A
<0.001
Ter ary methods
23
1.49 [0.95, 2.03]
[-1.06, 4.03]
<0.001
Heterogeneity: I2 = 85.25%; Q = 210.11; t2 = 1.68 and df =31
← Favours control
Favours experimental→
Standardised mean diﬀerence (mean ± 95% CI)
-7.5
-5.0
-2.5
0.0
2.5
5.0
7.5
and 95% PI)
Fig. 3  Forest plots showing the SMD (mean [95% CI and 95% PI]) 
in post-intervention 0–30 m sprint performance between intervention 
and control athletes. Bold formatting indicates p < 0.05. CI confi-
dence interval, N/A fewer than three training groups available, PI pre-
diction interval, SMD standardised mean difference
267
Training Medium to Long Sprint Performance in Football Athletes
3.4.1  Within‑Group Changes (0–30 m)
The sport-only and primary methods training failed to show 
statistical significance for change in 0–30 m performance. 
Significant performance improvements were observed in the 
combined and secondary methods training groups (moderate 
SMD) and tertiary methods (small SMD).
The combined, secondary, and tertiary methods demon-
strated a significantly larger training effect than sport-only 
training. Only secondary methods reported a significantly 
larger training effect than primary training methods.
3.4.2  Pairwise Between‑Group Differences (0–30 m)
The combined and primary training methods failed 
to show statistical significance to sprint performance 
changes compared with sport-only training. Significant 
performance improvements were observed (large SMD) 
for the secondary and tertiary training groups compared 
with the sport-only control groups. Between-experimental 
subgroups analysis failed to show statistical significance 
between training methods. Between-experimental-sub-
group analysis was not conducted on the primary or sec-
ondary subgroups with control groups because only two 
training groups were available.
3.5  SMD for 0 to > 30 m Performance
For 0 to > 30 m performance, 43 within-training group 
effects were analysed from 18 original studies [35, 61, 68, 
76–78, 83–85, 89, 92, 101, 109–112, 114, 116]. Eight train-
ing and control groups from five studies were eligible for 
a pairwise between-group analysis (sport-only control vs. 
experimental) [35, 61, 68, 92, 114]. The five available con-
trol groups were split in three studies [35, 61, 68] to allow 
comparison between multiple training groups in the studies 
[69]. Figures 4, 5 show the SMD for each training type.
Training type
Training
groups (n)
SMD (95% CI)
95% PI
p-value
Sport only 
5
0.22 [-0.10, 0.54]
[-0.97, 1.41] 
0.18
Combined methods
18
0.33 [0.14, 0.52]
[-0.50, 1.16] 
<0.001
Primary methods
2
0.06 [-0.14, 0.25]
N/A
0.57
Secondary methods
5
0.37 [0.25, 0.50]
N/A*
<0.001
Ter ary methods
12
0.22 [-0.26, 0.70]
[-1.73, 2.17]
0.37
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
← Reduced sprint performance
Increased sprint performance→
Standardised mean diﬀerence (mean ± 95% CI and 95% PI)
Heterogeneity: I2 = 93.16%; Q = 599.021; t2 = 0.24 and df = 42
a
a
Fig. 4  Forest plots showing the SMD (mean [95% CI and 95% PI]) 
for the studies evaluating the between-training-group effects on 0 to 
> 30 m sprint performance. aSignificantly different to primary train-
ing methods, p < 0.05. Bold formatting indicates p < 0.05. CI confi-
dence interval, N/A fewer than three training groups available, N/A* 
all studies show a common effect size, PI prediction interval, SMD 
standardised mean difference
Training type
Training
groups (n)
SMD (95% CI)
95% PI
p-
value
Combined methods
4
1.51 [0.21, 2.80]
[-4.26, 7.28] 
0.02
Ter ary methods
4
1.12 [0.56, 1.68]
[-0.29, 2.52]
<0.001
Heterogeneity: I2 = 60.25%; Q = 17.61; t2 = 0.50 and df =7
← Favours control
Favours experimental→
Standardised mean diﬀerence (mean ± 95% CI
-5
-2.5
0
2.5
5
7.5
and 95% PI)
Fig. 5  Forest plots showing the SMD (mean [95% CI and 95% PI]) in post-intervention 0 to > 30 m sprint performance between intervention and 
control athletes. Bold formatting indicates p < 0.05. CI confidence interval, PI prediction interval, SMD standardised mean difference
268
 
B. Nicholson et al.
3.5.1  Within‑Group Changes (0 to > 30 m)
The sport-only training, primary, and tertiary methods failed 
to show statistical significance for change in 0 to > 30 m 
sprint performance. Significant performance improvements 
were observed in the combined and secondary methods 
training groups (small SMD). Between-subgroups analy-
sis failed to show statistical significance between training 
methods. Between-subgroup analysis was not conducted on 
the primary training methods subgroup as only two training 
groups were available.
3.5.2  Pairwise Between‑Group Differences (0 to > 30 m)
Significant performance improvements were observed 
(large SMD) for the combined and tertiary training groups 
compared with the sport-only control groups. Between-
experimental subgroups analysis failed to show statistical 
significance between training methods.
3.6  SMD for Maximum‑Velocity Phase Performance
For Vmax-phase performance, 31 within-training group 
effects were analysed from 13 original studies [34, 58, 67, 
68, 76, 81, 93, 97, 110–112, 114, 116]. Eight training and 
control groups from five studies were eligible for a pairwise 
between-group analysis (sport-only control vs. experimen-
tal) [58, 67, 68, 97, 114]. The five available control groups 
were split in three studies [58, 67, 68] to allow comparison 
between the multiple training groups in the studies [69]. Fig-
ures 6, 7 show the SMD for each training type.
Training type
Training
groups (n)
SMD (95% CI)
95% PI
p-
value
Sport only 
5
0.19 [-0.45, 0.83]
[-2.33, 2.71]
0.57
Combined methods
9
0.05 [-0.23, 0.34]
[-1.01, 1.11]
0.73
Primary methods
3
-0.07 [-0.24, 0.10]
[-1.75, 1.61]
0.43
Secondary methods
3
0.07 [-0.10, 0.23]
[-1.57, 1.71]
0.43
Ter ary methods
11
0.45 [0.08, 0.81]
[-0.97, 1.83]
0.02
-2.5 -2 -1.5 -1 -0.5
0
0.5
1
1.5
2
2.5
3
← Reduced sprint performance
Increased sprint performance→
Heterogeneity: I2 = 94.82%; Q = 578.74; t2 = 0.23 and df = 30
a
Standardised mean diﬀerence (mean ± 95% CI and 95% PI)
Fig. 6  Forest plots showing the SMD (mean [95% CI and 95% PI]) 
for the studies evaluating the between-training-group effects on Vmax-
phase sprint performance. aSignificantly different to primary training 
methods, p < 0.05. Bold formatting indicates p < 0.05. CI confidence 
interval, PI prediction interval, SMD standardised mean difference, 
Vmax maximum sprinting velocity
Training type
Training
groups (n)
SMD (95% CI)
95% PI
p-
value
Combined methods
2
-0.83 [-4.33, 2.68]
N/A
0.64
Primary methods
2
1.13 [0.17, 2.09]
N/A
0.02
Secondary methods
1
1.27 [-0.11, 2.65]
N/A
0.07
Ter ary methods
3
1.95 [0.75, 3.15]
[-10.13, 14.03]
<0.001
Heterogeneity: I2 = 89.47%; Q = 66.49; t2 = 3.40 and df =7
← Favours control
Favours experimental→
Standardised mean diﬀerence (mean ± 95% CI)
-12.5 -10 -7.5 -5 -2.5
0
2.5
5
7.5 10 12.5 15
and 95% PI)
Fig. 7  Forest plots showing the SMD (mean [95% CI and 95% PI]) 
in post-intervention Vmax-phase sprint performance between interven-
tion and control athletes. Bold formatting indicates p < 0.05. CI confi-
dence interval, N/A fewer than three training groups available, PI pre-
diction interval, SMD standardised mean difference, Vmax maximum 
sprinting velocity
269
Training Medium to Long Sprint Performance in Football Athletes
3.6.1  Maximum‑Velocity Phase Within‑Group Changes
The sport-only training, primary, secondary, and combined 
methods failed to show statistical significance for change 
in Vmax-phase performance. The tertiary training methods 
showed a significant moderate performance improvement. 
The tertiary training methods demonstrated a significantly 
larger training effect than primary training methods.
3.6.2  Maximum‑Velocity Phase Pairwise Between‑Group 
Differences
The secondary and combined training methods failed to 
show statistical significance to sprint performance change 
to sport-only training. Significant performance improve-
ments were observed (large SMD) for the primary and 
tertiary methods training groups compared with the sport-
only control groups. Between-subgroup analysis was not 
conducted as the tertiary methods were the only training 
group with more than two training groups available.
3.7  Within‑Group Change Design vs. Pairwise 
Between‑Group Effect
No significant difference was observed for the combined 
methods subgroups (all distance outcomes). Both signifi-
cant (Vmax phase) and non-significant (0–30 m) differences 
were found for the primary training between-subgroup 
analysis. The between-group effect from comparative trials 
was significantly larger for both tertiary (all distance out-
comes) and secondary methods (0–30 m and Vmax phase) 
(Table 5).
3.8  Heterogeneity
The degree of overall heterogeneity was high for all out-
come measures between studies I2 (> 75%).
3.9  Sensitivity Analysis
Omitting each study separately identified the effect that each 
study had on the mean effect. This revealed minor changes 
only for the secondary training methods. These changes did 
not have a substantial impact on the statistical significance 
of the overall mean effect. Sport-only, combined, primary, 
and tertiary training methods were sensitive to the exclusion 
of one or more studies independently and, in turn, moder-
ated the statistical interpretation of the results. Removal of 
one of the five 0 to > 30 m studies [35] from the sport-only 
methods subgroup moderated the within-group change sta-
tistical significance from non-significant (p > 0.05) to signifi-
cant (p < 0.05). Removal of one of the five 0–30 m studies 
[97] and one of two Vmax-phase studies from the pairwise 
between-group differences (sport-only vs. combined train-
ing methods) moderated the statistical significance from 
non-significant (p > 0.05) to significant (p < 0.05). Removal 
of one of the four 0 to > 30 m studies [35] from the pair-
wise between-group differences (sport-only vs. combined 
training methods) moderated the statistical significance 
from significant (p < 0.05) to non-significant (p > 0.05). 
Removal of two of the five 0–30 m studies [58, 60] and 
one of three Vmax-phase studies [58] from the within-group 
change primary methods subgroup moderated the statisti-
cal significance from non-significant (p > 0.05) to signifi-
cant (p < 0.05). Removing one of two 0–30 m Vmax-phase 
primary methods subgroup studies [58] from the pairwise 
between-group differences (primary vs. combined training 
methods) moderated the statistical significance from non-
significant (p > 0.05) to significant (p < 0.05). Removing one 
of the eight within-group 0 to > 30 m studies [89] and one 
of the six Vmax-phase studies [81] from the tertiary training 
method subgroup moderated the statistical significance from 
non-significant to significant and from significant to non-
significant, respectively.
3.10  Evaluation of Small Study Effects
Inspection of the funnel plots for the within-group change 
revealed the presence of a statistically significant Egger’s 
regression intercept, showing evidence of small study 
effects for the 0–30 m (intercept 9.36; 95% CI 5.68–13.04; 
p < 0.001) and Vmax-phase (intercept 11.38; 95% CI − 4.88 
to 17.87; p < 0.01). For studies included in the pairwise 
between-group differences comparison, evidence indicated 
small study effects for the 0–30 m (intercept 8.90; 95% CI 
4.22–13.21; p < 0.001), 0 to > 30 m (intercept 6.60; 95% 
CI − 0.10 to 13.27; p = 0.05), and Vmax-phase (intercept 
15.83; 95% CI − 3.15 to 28.14; p = 0.02). The SMD between 
pre- and post-intervention sprint performance was therefore 
not considered symmetrical, suggesting the presence of sig-
nificant publication bias [121]. However, there was little 
Table 5  Subgroup analysis comparing the within-group change 
standardised mean difference in sprint performance and pairwise 
between-group effect from comparative trials
↑ indicates that the pairwise between-group effect standardised mean 
difference was significantly larger (p < 0.05) than the within-group 
change in sprint performance
Subgroup within study
0–30 m
0 to > 30 m
Vmax phase
Combined methods
p = 0.85
p = 0.08
p = 0.63
Primary methods
p = 0.79
NA
↑p = 0.02
Secondary methods
↑p < 0.01
NA
↑p = 0.01
Tertiary methods
↑p < 0.001
↑p = 0.02
↑p = 0.02
270
 
B. Nicholson et al.
evidence to indicate a small study effect for the within-group 
change in the 0 to > 30 m outcome studies (intercept 3.69; 
95% CI − 1.90 to 9.28; p = 0.19).
Table 6  Summary of moderator variable analysis for football code, sex, playing standard, age, and phase of training meta-analysis by subgroup 
with the sport-only training groups removed
Between-group differences
Subgroup standardised mean difference
Football code
0–30 m
Soccer vs. rugby league, p = 0.07
Soccer vs. rugby union, p = 0.98
Rugby league vs. rugby union, p = 0.10
American  footballa
Rugby  sevensa
0 to > 30 m
American football vs. rugby league, p = 0.47
American football vs. rugby sevens, p = 0.31
American football vs. rugby union, p = 0.08
American football vs. soccer, p = 0.34
Rugby league vs. rugby union, p = 0.59
Rugby league vs. rugby sevens, p = 0.64
Rugby league vs. soccer, p = 0.37
Rugby sevens vs. rugby union, p = 0.49
Rugby sevens vs. soccer, p = 0.02*
Rugby union vs. soccer, p < 0.001*
Australian  footballa
Vmax phase
Rugby sevens vs. soccer, p = 0.16
Australian  footballa
Soccer
0–30 m (n = 62; SMD 0.47; 95% CI 0.34–0.59; 95% PI − 0.55 to 1.48); p < 0.001*
0 to > 30 m (n = 21; SMD 0.49; 95% CI 0.30–0.68; 95% PI − 0.41 to 1.39); p < 0.001*
Vmax (n = 14; SMD 0.32; 95% CI 0.02–0.62; 95% PI − 0.45 to 1.43); p = 0.04*
Rugby union
0–30 m (n = 6; SMD 0.46; 95% CI 0.18–0.74; 95% PI − 0.50 to 1.42); p < 0.001*
0 to > 30 m (n = 4; SMD 0.07; 95% CI − 0.02 to 0.16; 95% PI − 0.12 to 0.26); p = 0.12
Vmax (NA)
American football
0–30 m (NA)
0 to > 30 m (n = 3; SMD 0.33; 95% CI 0.06–0.60; 95% PI − 2.43 to 3.08); p = 0.02*
Vmax (NA)
Rugby league
0–30 m (n = 4; SMD − 0.06; 95% CI − 0.60 to 0.48; 95% PI − 2.64 to 2.53); p = 0.84
0 to > 30 m (n = 3; SMD − 0.39; 95% CI − 2.30 to 1.53; 95% PI − 25.17 to 24.39); p = 0.69
Vmax (NA)
Rugby sevens
*0–30 m (n = 1; SMD 0.43; 95% CI 0.17–0.69); p < 0.01*
0 to > 30 m (n = 4; SMD 0.15; 95% CI − 0.06 to 0.36; 95% PI − 0.58 to 0.88); p = 0.16
Vmax (n = 4; SMD 0.08; 95% CI − 0.06 to 0.22; 95% PI − 1.37 to 2.34); p = 0.27
Australian Football
0–30 m (NA)
0 to > 30  ma (n = 2; SMD − 0.14; 95% CI − 0.39 to 0.12); p = 0.29
Vmax
a (n = 2; SMD 0.09; 95% CI − 0.07 to 0.24); p = 0.27
Sex
0–30 m
Male vs. female, p = 0.15
0 to > 30 m
Male vs. female, p = 0.77
Vmax phase
Male vs. female, p = 0.17
Male
0–30 m (n = 74; SMD 0.38; 95% CI 0.26–0.49; 95% PI − 0.59 to 1.35); p < 0.001*
0 to > 30 m (n = 34; SMD 0.30; 95% CI 0.11–0.48; 95% PI − 0.81 to 1.41); p < 0.001*
Vmax (n = 23; SMD 0.22; 95% CI 0.02–0.42; 95% PI − 0.41 to 1.38); p = 0.03*
Female
0–30 m (n = 7; SMD 0.64; 95% CI 0.30–0.97; 95% PI − 0.54 to 1.81); p < 0.001*
0 to > 30 m (n = 3; SMD 0.25; 95% CI 0.00–0.50; 95% PI 2.53–3.03); p = 0.05
Vmax (n = 3; SMD 0.02; 95% CI − 0.18 to 0.22; 95% PI − 4.99 to 5.96); p = 0.84
Playing standard
0–30 m
Elite vs. sub-elite, p = 0.21
0 to > 30 m
Elite vs. sub-elite, NA
Vmax phase
Elite vs. sub-elite, p = 0.55
Elite
0–30 m (n = 52; SMD 0.39; 95% CI 0.25–0.53; 95% PI − 0.60 to 1.38); p < 0.001*
0 to > 30 m (n = 36; SMD 0.28; 95% CI 0.10–0.45; 95% PI − 0.39 to 1.36); p < 0.001*
Vmax (n = 22; SMD 0.21; 95% CI 0.00–0.42; 95% PI …); p = 0.04*
Sub-elite
0–30 m (n = 16; SMD 0.58; 95% CI 0.32–0.85; 95% PI − 0.59 to 1.75); p < 0.001*
0 to > 30 m (NA)
Vmax (n = 4; SMD 0.10; 95% CI − 0.18 to 0.22; 95% PI − 1.37 to 2.34); p = 0.48
Age
0–30 m
Senior vs. youth, p = 0.07
0 to > 30 m
Senior vs. youth, p = 0.24
Vmax phase
Senior vs. youth, p = 0.37
Senior
0–30 m (n = 44; SMD 0.51; 95% CI 0.34–0.68; 95% PI − 0.63 to 1.65); p < 0.001*
0 to > 30 m (n = 25; SMD 0.19; 95% CI 0.08–0.31; 95% PI − 0.40 to 1.38); p < 0.001*
Vmax (n = 21; SMD 0.25; 95% CI 0.03–0.47; 95% PI − 0.41 to 1.39); p = 0.03*
Youth
0–30 m (n = 35; SMD 0.32; 95% CI 0.20–0.44; 95% PI − 0.41 to 1.05); p < 0.001*
0 to > 30 m (n = 12; SMD 0.48; 95% CI 0.03–0.94; 95% PI − 0.47 to 1.45); p = 0.04*
Vmax (n = 5; SMD 0.00; 95% CI − 0.23 to 0.23; 95% PI − 0.88 to 1.86); p = 0.98*
271
Training Medium to Long Sprint Performance in Football Athletes
3.11  Moderator Variables
Table 6 presents the subgroup analysis assessing potential 
moderating factors for sprint performance (0–30 m, 0 to 
> 30 m performance, and Vmax-phase). The between-sub-
group analysis showed significant (p < 0.05) differences for 
football code, age, and phase of training; all moderated the 
overall magnitude of training effects (either smaller or larger 
SMD). However, the between-subgroup differences were not 
consistent across distance outcomes. Both playing standard 
and sex consistently demonstrated no significant difference 
between subgroups.
4  Discussion
4.1  Overview of the Main Findings
Multiple training methods are recommended for improving 
medium- to long-distance sprint performance because of its 
importance in the football codes [32, 33, 40–49]. This sys-
tematic review with meta-analysis is the first to (1) analyse 
the impact of different methods in enhancing medium- to 
long-distance sprint performance outcomes (0–30 m, 0 to 
> 30 m, and the Vmax phase) within football code athletes 
and (2) identify how moderator variables (i.e., football code, 
sex, playing standard, age, and phase of season) affected the 
training response. This review analysed 60 studies [34–36, 
58–68, 75–120], totalling sprint performance measurements 
from 1500 athletes, thus providing the largest systematic 
evidence base for enhancing medium- to long-distance sprint 
performance over distances > 20 m exclusively including 
football code athletes.
In summary, the meta-analysis of all the included studies 
showed enhanced sprint performance in the combined, sec-
ondary, and tertiary training methods groups. Combined and 
secondary methods showed small to moderate improvements 
in 0–30 m and 0 to > 30 m performance. Tertiary methods 
showed small and moderate performance improvements in 
both 0–30 m and Vmax-phase outcomes, respectively. Signifi-
cant performance improvements (large SMD) were observed 
for the combined (0 to > 30 m), primary (Vmax phase), sec-
ondary (0–30 m), and tertiary methods (all outcomes) when 
compared pairwise with the sport-only control groups. 
These findings support previous literature that stated that 
the medium to long sprint performance of football code ath-
letes can be enhanced concurrently alongside football code-
specific training [25, 41]. Despite several training methods 
demonstrating significant improvement in sprint perfor-
mance, it is important to note that the PIs contained both 
null and negative effects in all training groups. This indi-
cates that, for all training subgroups and assuming a normal 
distribution of the data, some athletes experienced null or 
negative performance effects even though the point estimate 
suggested benefit. Sport-only training showed no significant 
change in medium to long sprint performance, suggesting 
such training alone is insufficient to improve performance. 
The significant differences in between-group effect compari-
sons for studies with control groups and the within-group 
change consistently demonstrated a larger effect. Despite 
Subgroup analyses showing the SMD (mean; 95% CI and 95% PI) between post and pre-intervention sprint performance outcomes. Some stud-
ies were not included because the value used for subgroup analysis was not reported or did not match the appropriate categories. PI were not 
included for subgroups with fewer than three training groups
CI confidence interval, NA no training group met the inclusion criteria, PI prediction interval, SMD standardised mean difference, Vmax maxi-
mum velocity-phase sprint performance outcome
a Fewer than three training groups
*p < 0.05
Table 6  (continued)
Between-group differences
Subgroup standardised mean difference
Phase
0–30 m
In-season vs. off-season, p = 0.91
In-season vs. pre-season, p = 0.13
Pre-season vs. off-season, p = 0.33
0 to > 30 m
In-season vs. off-season, p < 0.10
In-season vs. pre-season, p < 0.09
Pre-season vs. off-season, p = 0.11
Vmax phase
In-season vs. pre-seaso,n p = 0.36
In-season
0–30 m (n = 41; SMD 0.32; 95% CI 0.16–0.48; 95% PI − 0.72 to 1.36); p < 0.001*
0 to > 30 m (n = 11; SMD 0.64; 95% CI 0.38–0.89; 95% PI − 0.49 to 1.46); p < 0.001*
Vmax (n = 10; SMD 0.28; 95% CI − 0.14 to 0.71; 95% PI − 0.51 to 1.48); p = 0.19
Off-season
0–30 m (n = 4; SMD 0.29; 95% CI − 0.13 to 0.71; 95% PI − 1.73 to 2.31); p = 0.18*
0 to > 30 m (n = 3; SMD 0.33; 95% CI 0.06–0.60; 95% PI − 4.99 to 5.96); p = 0.02*
Vmax (NA)
Pre-season
0–30 m (n = 26; SMD 0.52; 95% CI 0.31–0.73; 95% PI − 0.58 to 1.62); p < 0.001*
0 to > 30 m (n = 17; SMD 0.02; 95% CI − 0.23 to 0.26; 95% PI − 0.43 to 1.41); p = 0.94
Vmax (n = 8; SMD 0.08; 95% CI − 0.04 to 0.19; 95% PI − 0.57 to 1.54); p = 0.19
272
 
B. Nicholson et al.
sprint measures over > 20 m being a proxy measure of Vmax 
improvements, changes in performance may not result exclu-
sively from Vmax-specific adaptations. Instead, performance 
changes in outcomes > 20 m may be attributed to either or 
both adaptations specific to the acceleration or Vmax phases. 
Between-subgroup analysis identified that football code, age, 
and phase of training all moderated the overall magnitude 
of training effects (either smaller or larger SMD). However, 
the between-subgroup differences were not consistent across 
distance outcomes. The increase in performance was signifi-
cantly greater for soccer than for rugby union, rugby sevens, 
and American football for 0 to > 30 m, whereas the improve-
ment in performance was significantly greater for American 
football than for rugby union (0 to > 30 m). The increase 
in performance was significantly greater for youth athletes 
than for senior athletes (0 to > 30 m). In-season performance 
changes were significantly greater than in the pre-season and 
off-season periods in the 0 to > 30 m outcomes only. Playing 
standard and sex consistently demonstrated no significant 
difference between subgroups. The lack of consistency may 
suggest greater importance of other moderator variables, 
such as training and load prescription (e.g., mode, volume, 
intensity, and frequency), over the described individual 
population characteristics.
4.2  Summary of Interventions to Develop Sprint 
Performance
The 60 studies were categorised into five training modes, 
resulting in 111 training groups (i.e., sport only, n = 27; com-
bined methods, n = 35; primary methods, n = 8; secondary 
methods, n = 9; tertiary methods, n = 59). Of the 60 studies, 
26 had sport-only comparator groups [35, 36, 58–68, 75, 82, 
88, 90, 92, 95–97, 104, 106, 113, 114, 119], which provided 
41 training groups for between-group effect comparisons 
(combined methods, n = 9; primary methods, n = 3; second-
ary methods, n = 2; tertiary methods, n = 27). No research 
met the inclusion criteria for the combined specific training 
methods group, which combined both primary and second-
ary training methods. These findings highlight the volume 
of tertiary method training studies and the reported gap in 
the available literature to support specific sprint-training 
methods (primary, secondary, and combined specific train-
ing methods) in football code athletes [33, 44]. This also fur-
ther supports the requirement for the within-group analysis, 
including a greater range of study designs given the small 
number of studies with a sport-only control group avail-
able. The scarcity of specific sprint-training method studies 
is most probably because football code training typically 
consists of tertiary training methods to develop the multiple 
physical capacities (e.g., strength, speed, power) required 
within these sports. This is a strength of the current study, 
as previous reviews [32, 33] did not include all training 
undertaken by the intervention groups within their analysis 
(e.g., primary or secondary training groups also completing 
tertiary training methods or vice versa [94, 117, 122, 123]).
The current degree of overall heterogeneity was high for 
all outcome measures between studies (I2 > 75% [124]). Het-
erogeneity is to be expected in systematic reviews given the 
grouping of both clinically and methodologically diverse 
studies [124]. The high degree of heterogeneity reflects 
the diversity of the training effects presented. This is likely 
due to the wide variation in the intervention characteristics, 
including training frequency [78, 80], intensity [34, 36, 59, 
125], duration [76], volume [109], other training completed 
[62, 100]), population characteristics (e.g., sex [65], base-
line physical characteristics [60, 110], training experience 
[34, 80]), sprint monitoring methods (e.g., start position, 
environmental factors [56]), and technology (e.g., equipment 
[58]). Therefore, these findings should be interpreted care-
fully as the variation of the effect sizes demonstrates that 
training response is highly individualised.
The quality of the studies was high (18 ± 1.9; range 
11–20) because most studies provided clearly described 
research methodology, enabling practitioners and/or 
researchers to replicate or build on research findings reli-
ably [126]. A methodological study scale used to evaluate 
research conducted in athletic-based training environments 
[54] showed that, to increase the quality of future studies, 
researchers should randomise participants, include a control 
group, and provide a detailed results section. The inclusion 
of detailed information on additional training conducted in 
applied settings is important for the understanding of the 
training intervention undertaken and to fully assess whether 
any outside interactions with any adaptations were seen fol-
lowing a training intervention [127].
Most training interventions reported positive effects on 
sprinting capabilities, which suggests that sprint perfor-
mance outcomes can easily be improved with a variety of 
methods. However, this needs to be considered from the 
context of the literature base and the relative importance 
of phase-specific adaptations. Included studies represented 
both youth and senior athletes from elite and sub-elite 
cohorts, with the majority having limited previous system-
atic exposure to the intervention methods [58, 80, 82, 85, 
89, 95, 114]. Based on the dose–response relationship and 
the principle of diminishing returns, athletes with a rela-
tively low training age are more likely to have greater train-
ing responses [128–130]. However, as previously reported 
[33], this does not appear to be the case for the Vmax phase 
or highly trained populations. Highly trained athletes have 
demonstrated that mean annual within-athlete sprint per-
formance differences are lower than typical variations, or 
smallest worthwhile change, and the influence of external 
conditions (e.g., wind, temperature, altitude, timing meth-
ods/procedures [56, 130]). Inspection of the funnel plot and 
273
Training Medium to Long Sprint Performance in Football Athletes
Egger’s regression intercept identified evidence of small 
study effects in the 0–30 m and Vmax-phase performance 
outcomes. The SMDs between pre- and post-intervention 
sprint outcomes were not considered symmetrical, suggest-
ing the presence of significant publication bias. While publi-
cation bias towards studies reporting positive outcomes may 
be involved, another plausible explanation is the lack of a 
control group in many studies, as the results might have been 
affected by learning effects or the football code training in 
the intervention period.
4.3  Subgroup Analyses of Training Methods
The principle of specificity [137, 138] was used to categorise 
the training intervention subgroups (i.e., sport only, primary, 
secondary, tertiary, and combined). Primary methods pre-
sent the greatest specificity by simulating the sprint move-
ment pattern [131], whereas the secondary methods are less 
specific, involving overloaded sprinting actions. The tertiary 
training methods included strength, power, and plyometric 
training, which are considered the least ‘specific’ to sprint 
performance as these methods are commonly performed 
to target neuromuscular adaptations rather than simulating 
movement mechanics [132]. The extent to which the method 
impacts on and ‘transfers’ to sprint performance ultimately 
determines the quality of a training programme to improve 
athletic performance [133].
The factors underpinning the development of sprint per-
formance appear to be consistent across sports [134–140]. 
Practitioners can target the determinants of performance, 
such as optimising the sequencing of stride length and fre-
quency, enhancing the athlete’s physical capacities relative 
to BM (e.g., lower limb force–velocity–power; stiffness) 
and increasing the mechanical effectiveness of force appli-
cation [134, 136, 138, 140–145]. These methods provide 
practitioners with multiple methods of developing sprinting 
performance [130, 144, 146]. Performance improvements 
result from specific transferable training adaptations typi-
cally categorised as neural or morphological (architectural 
or structural) factors [26, 146–149]. However, training 
effects appear to be mode specific, with distance-specific 
performance changes (e.g., 0–30 and 0 to > 30 m) associated 
with phase-specific adaptations [32]. Although the factors 
underpinning sprint development are consistent, phase-spe-
cific differences in both kinetic and kinematic variables are 
clear [26]. The importance of mechanical variables appears 
to shift as sprint distance increases (e.g., greater associa-
tion between theoretical maximal force generation in shorter 
sprints vs. greater associations in maximum theoretical 
velocity force can be applied in longer sprints [150]). There-
fore, it is important to consider the phase-specific adapta-
tions that may be present across medium- to long-distance 
sprint outcomes.
Despite researchers and practitioners typically using out-
come measures over distances > 20 m as a proxy measure of 
Vmax-phase capabilities, performance changes may be attrib-
uted to either or both adaptations specific to the acceleration 
or Vmax phases, not the Vmax phase exclusively. This is evi-
dent as the Vmax phase presented performance changes that 
were distinctly different from both the 0–30 m and the 0 to 
> 30 m outcomes. Although the acceleration and Vmax phases 
are related [8, 132, 150–153], separate physical capacities 
and mechanical parameters determine sprint performance 
[27, 29, 129, 137, 140, 154–156]. Research has demon-
strated that football code athletes can attain Vmax-phase 
sprinting patterns at distances ≤ 20 m [6–10, 29]. Therefore, 
after 20 m, there is likely an increasing influence of the Vmax 
phase, with the time spent increasing with distance. There-
fore, given the sequential phases of sprinting, both 0–30 
and 0 to > 30 m outcomes will be influenced by changes 
in acceleration, with the 0 to > 30 m outcome influenced 
to a lesser extent (more time performing Vmax sprinting 
patterns), whereas the Vmax-phase flying sprint split times 
and Vmax assessments do not include, or include a limited, 
acceleration phase. Hence, it is important to emphasize that, 
although the sequential phases are related, different factors 
affect performance in each phase. Therefore, training pro-
tocols to develop each of these phases must also differ [33]. 
This was evident in both the secondary and the combined 
methods training groups. Hence, when including all stud-
ies, both training methods presented a significant improve-
ment in both 0–30 and 0 to > 30 m performance, whereas 
they produced non-significant trivial changes in Vmax-phase 
performance. Therefore, practitioners should also consider 
the mechanical and neuromuscular requirements that shift 
across the sub-phases (acceleration, maximal speed, and 
maintenance) of medium- to long-distance sprint outcomes 
and the implications of these for training phase-specific per-
formance [26, 150, 154, 157].
4.3.1  Sport‑Only Training
Sport-only training focuses on the development of techni-
cal and tactical performance within football and does not 
include any specific or non-specific sprint training. The 
meta-analysis showed that sport-only training groups did not 
significantly change sprint performance [35, 36, 58–68, 75, 
82, 88, 90, 95–97, 104, 106, 113, 114, 119]. Football code 
training is characterised by multidirectional and intermittent 
bouts of high-intensity running and sprinting interspersed 
with bouts of moderate- and low-intensity activity (e.g., 
jogging, walking, and repositioning [158–161]). Therefore, 
although football code training may involve athletes repeat-
edly performing short sprints (e.g., 5–20 m, 2–3 s) during 
and between sport-specific actions [2, 23, 158, 159, 162], 
this most likely has limited or no very-high-speed or sprint 
274
 
B. Nicholson et al.
threshold running [160, 161, 163]. Such training methods do 
not meet the recommendations that athletes be exposed to 
multiple sprints where they maximally accelerate to achieve 
and maintain Vmax with complete recovery between efforts 
to effectively enhance sprint performance [130]. Further 
explanations could include residual fatigue and the interfer-
ence effect affecting maximal force and velocity outcomes 
within sport-only practices [130, 164–166]. Therefore, evi-
dence suggests that sport-only training alone is insufficient 
to improve medium to long sprint performance, and football 
code practitioners should consider this within their planning 
and delivery of training.
4.3.2  Primary Methods
Primary methods simulate the sprint movement pattern (e.g., 
sprint-technique drills, stride length and frequency exercises, 
and sprints of varying distances and intensities). The com-
bined exposure of large forces (> 2 × BM) produced over 
short ground-contact periods (~ 0.08 to ~ 0.20 s) performed 
at high movement velocities (7–10 m·s−1) while maxi-
mally sprinting results in both a coordinative overload and 
high neuromuscular stimulation [134, 136–138, 140, 155, 
167]. Therefore, exposure to maximal sprinting is expected 
to facilitate chronic physical adaptations and enhanced 
mechanical efficiency to improve sprint performance [133, 
134, 136–138, 140, 167]. However, no studies have meas-
ured chronic kinematic changes over distances > 20 m in 
response to primary training methods (no additional tertiary 
methods) to support their use in football code athletes [67, 
101]. Our findings suggest that primary training methods 
[58, 60, 67, 86, 101, 102, 116] may not significantly improve 
sprint performance and—in some cases—may impair per-
formance. The primary methods within-group changes 
presented no significant change in sprint performance (i.e., 
0–30 m, SMD 0.20 [95% CI − 0.01 to 0.42; 95% PI − 0.51 to 
0.92]; 0 to > 30 m, SMD 0.06 [95% CI − 0.14 to 0.25; 95% 
PI not applicable as n < 3]; Vmax SMD − 0.07 [95% CI − 0.24 
to 0.10; 95% PI − 1.75 to 1.61]). This was further supported 
by the pairwise between-group comparisons (sport only vs. 
primary), which confirmed no significant difference was evi-
dent in the 0–30 m: SMD 0.33 (95% CI − 0.58 to 1.25; 95% 
PI not applicable as n < 3). Despite the Vmax-phase outcome 
reporting, the primary methods were superior (large SMD) 
to sport-only training (SMD 1.13 [95% CI 0.17–2.09; 95% 
PI not applicable as n < 3]), and this difference reflects the 
maintenance of sprint performance rather than the reduced 
performances reported in the sport-only groups [58, 67]. The 
contradictions between our findings and previous reviews 
supporting primary training methods is likely because other 
studies misclassified training methods by not including addi-
tional training (e.g., resistance training), most probably as 
part of their usual training programme [38, 117, 168–171]. 
Therefore, previous review findings may support a combined 
approach of both specific and non-specific training, not pri-
mary training alone [32, 33].
Football code athletes have high chronic exposure to short 
sprints (< 20 m) with incomplete recovery between sprints 
as part of the demands of training and matches; therefore, 
replicating these exposures is unlikely sufficient stimulus for 
neurological or morphological adaptations [158–161, 172]. 
Prescribing short-sprint repetition distances (e.g., 18.7–20 m 
[58, 60, 116]) limits athlete exposure to sprinting at Vmax 
(typically achieved at > 20 m in football code athletes [8, 
9, 27, 29–31]), performed at submaximal efforts (< 95% 
Vmax [102]) and/or with incomplete recovery (e.g., 2–3 min 
between repetitions [< 1–2 min of  activity−1]) for medium 
to long sprints (e.g., 30–55 m sprints, ~ 4–7 s duration [67, 
86, 102]). Furthermore, incomplete rest between sprint 
efforts may reduce maximal sprint intensity, causing meta-
bolic stress and reduction in energy substrates [173–175]. 
However, it is worth noting that the removal of two studies 
[58, 60] that prescribed short sprints moderated the statisti-
cal significance for the 0–30 m and Vmax-phase outcomes 
from non-significant to significant. These findings contrast 
with the findings in short-sprint performance, indicating 
that longer sprints and Vmax-phase outcomes may be more 
susceptible to performance changes from primary training 
methods when prescribed appropriately [25]. Future stud-
ies should provide complete rest periods between maximal 
intensity sprints reaching and maintaining Vmax.
Running technique drills that simulate the sprinting action 
by isolating specific movements into more manageable com-
ponents [130, 176] are a component of primary training. For 
positive reinforcement of the technique, sprinting biome-
chanics must closely resemble the action and develop the 
athlete’s limiting factor(s) [131, 177]. However, technique 
drills (e.g., A and B drills) are often performed at much 
slower velocities than sprinting, potentially not replicating 
sprinting from a kinematic standpoint [178]. It has been 
questioned whether running drills have value, especially 
when performed incorrectly [179, 180]. However, as with 
short-distance sprint outcomes [25], no study has evaluated 
the effects of including/excluding sprint-technique drills in 
football code athletes, and explanations of the training pre-
scription are often limited. Therefore, sprint training that 
addresses the magnitude and rate of force production on the 
ground and the mechanical efficiency (e.g., tertiary or sec-
ondary methods) may be more appropriate [180].
4.3.3  Secondary Methods
Secondary training modalities apply overload to the sprint-
ing action by reducing (e.g., resisted sprinting) or increas-
ing (e.g., assisted sprinting) the movement speed, allowing 
athletes to reach supramaximal velocities. Across the seven 
275
Training Medium to Long Sprint Performance in Football Athletes
studies [58, 60, 79, 85, 86, 101, 116], findings showed a 
significant moderate within-group improvement in 0–30 m 
(SMD 0.61 [95% CI 0.31–0.91; 95% PI − 0.43 to 1.65]) 
and small improvements in 0 to > 30 m (SMD 0.37 [95% 
CI 0.25–0.50; 95% PI: all studies shared a common effect 
size]), with no significant changes in Vmax phase (SMD 0.07 
[95% CI − 0.10 to 0.23; 95% PI − 1.57 to 1.71]). These find-
ings are supported by the pairwise between-group analysis 
(sport only vs. control), confirming the effectiveness of the 
secondary methods (large SMD) in enhancing or maintain-
ing medium to long sprint performance, respectively, com-
pared with reductions in sport-only training groups (0–30 m, 
SMD 2.78 [95% CI 1.53–4.03; 95% PI not applicable as 
n < 3]) and Vmax phase (SMD 1.27 [95% CI − 0.11 to 2.65; 
95% PI not applicable as n < 3]). Training adaptations have 
been reported as being velocity change specific (%Vmax 
increase vs. reduction [181]), with variations in distance-
specific improvements for secondary methods (i.e., assisted 
vs. resisted) [116]. This is evident in both our findings and 
those of another review, reporting no significant improve-
ments in Vmax-phase outcomes in secondary training meth-
ods [33]. Hence, the improvements in 0–30 and 0 to > 30 m 
performance may be a result of acceleration-specific adap-
tations reflected in short-sprint improvements included in 
the sprint outcome. The overload of the secondary training 
methods results in neurological or morphological adapta-
tions, allowing greater generation of ground reaction forces 
and improved mechanical efficiency to enhance performance 
[33, 44].
Resisted sprints (i.e., loaded sleds) were shown to 
increase both stride length and frequency and lead to an 
acute increase in forward trunk lean (improved position 
to generate horizontal impulse) during sprints < 20 m in 
team sport athletes and university students [182–185]. In 
contrast, assisted methods demonstrated increased stride 
length and decreased stride frequency in track athletes [33, 
44], whereas reduced ground contact times were reported 
in football code athletes [101]. Studies measuring chronic 
temporospatial changes in response to secondary training 
methods (no additional tertiary methods) to support these in 
football code athletes are currently limited [101]. Of the two 
overload strategies, resisted sprint training [58, 60, 79, 85, 
86, 116] has received the greatest attention in the research 
on football code athletes despite significant improvements 
in both training methods (resisted [58, 60, 85, 86, 116], 
assisted [116], and a combination of both [101]). Currently, 
no study has reported a statistically superior training effect 
between assisted and resisted training modes, so which train-
ing mode is the most effective for developing sprint per-
formance remains unclear. Therefore, secondary training 
methods appear to be an effective method for coaches and 
athletes to improve 0–30 and 0 to > 30 m sprint performance 
outcomes. However, if the aim is to develop the Vmax-phase 
performance, then training strategies other than sled towing 
(e.g., weighted vests) may be needed to develop phase-spe-
cific adaptations. For example, vertical forces have a greater 
relative contribution to the Vmax phase [136, 137]. Acute 
kinematic differences suggest vertical force production when 
sprinting could be developed by undertaking training strate-
gies utilising weighted vests by providing a greater load on 
the eccentric braking phase at the beginning of the stance 
phase [185, 186], whereas sled towing is expected to pro-
vide a superior adaptation in horizontal force production 
[185, 187, 188]. Further research is required to determine 
the optimal load, loading strategy, and dose for performance 
enhancement, particularly for Vmax development.
4.3.4  Tertiary Methods
Tertiary training methods represent a wide range of training 
methods (e.g. strength, power, plyometrics [32, 189]) that 
are commonly performed to target neuromuscular adapta-
tions that determine sprint performance (e.g., force–veloc-
ity–power and force–velocity profile) rather than simulat-
ing movement mechanics [26, 130, 146, 150]. Using the 
load–velocity relationship, the appropriate resistance (body-
weight or external loads) limits either the maximum velocity 
or the force at which the maximum effort will occur, or both 
[190]. Therefore, practitioners are able to use force–veloc-
ity–power-orientated exercises in isolation or in combination 
(e.g., high force/low velocity vs. low force/high velocity vs. 
peak power load) to target load-specific adaptations [26, 130, 
146, 150].
Despite previous criticisms of tertiary training methods 
questioning the effectiveness of developing sprint perfor-
mance, significant within-group moderate improvements 
were found for the 0–30 m (SMD 0.38 [95% CI 0.23–0.53; 
95% PI − 0.63 to 1.41]) and Vmax-phase outcomes (SMD 
0.45 [95% CI 0.08–0.81; 95% PI − 0.97 to 1.83]). No sig-
nificant change was found for the 0 to > 30 m outcome 
when all studies were included (SMD 0.22 [95% CI − 0.26 
to 0.70; 95% PI − 1.73 to 2.17]). The significant within-
group changes in point estimate in the 0–30 m and Vmax 
outcomes were supported by significant findings in the 
pairwise between-group analysis (sport only vs. tertiary), 
with observed performance improvements (large SMD) 
confirming the effectiveness of the tertiary training meth-
ods in enhancing medium to long sprint performance com-
pared with sport-only training: 0–30 m (SMD 1.49 [95% 
CI 0.95–2.03; 95% PI − 1.06 to 4.03]), 0 to > 30 m (SMD 
1.12 [95% CI 0.56–1.68; 95% PI − 0.29 to 2.52]), and Vmax 
phase (SMD 1.95 [95% CI − 0.75 to 3.15; 95% PI − 10.13 to 
14.03]). Therefore, phase-specific adaptations may be pre-
sent. However, the presence of significant improvements in 
both 0–30 m (likely a greater influence of the acceleration 
phase) and the Vmax-phase performance changes are likely 
276
 
B. Nicholson et al.
a result of both acceleration- and Vmax-phase-specific adap-
tations. Research comparing the kinetic factors underlying 
differences between athletes with higher Vmax capabilities 
(sprinters) and slower athletes (soccer players), found that, 
at the same touchdown velocity, the sprinters attenuated the 
eccentric forces to a greater extent in the late braking phase 
and produced a higher antero‐posterior component of force, 
yet ground contact durations were similar across groups 
[27]. Therefore, training methods such as strength, power, 
or plyometrics training that increase an athlete’s ability to 
produce sufficient vertical force, to withstand and reverse 
eccentric braking forces, and to generate high antero‐pos-
terior propulsive force may be required to enhance an ath-
lete’s ability to accelerate more rapidly while also attaining 
a greater Vmax [27, 130]. The improved physical capacities 
developed during tertiary training methods have previously 
been shown to manifest in significant improvements in sprint 
performance with associated reductions in contact time or 
changes in stride frequency and length [34, 67, 169, 170]. 
Therefore, correspondence between the larger ground reac-
tion forces produced across medium- to long-distance sprints 
and the neural and morphological adaptations induced by 
these training methods is likely to be high [140]. Hence, 
the lack of significance in the 0–30 m outcomes is likely 
due to large significant reductions in sprint performance as 
presented by Gabbett et al. [89], moderating the statistical 
interpretation of the results and therefore supporting previ-
ous research [32] for the use of tertiary training methods 
(i.e., strength, power, and plyometric training) performed 
individually or in combination (e.g., strength power and 
plyometrics training) for improving sprint performance.
Considerations should be made when training for 
increased mass development, which is often associated with 
tertiary methods: as an athlete gets heavier they may not 
produce higher maximal force characteristics when normal-
ised for BM [132]. Therefore, the force requirements in the 
stance leg increase with BM to minimise the braking forces 
and maximise propulsive forces to attain Vmax, as does the 
aerodynamic drag resulting from a larger frontal surface area 
[132, 191]. Hence, increases in BM may be counterproduc-
tive for sprinting, at least when not moving an external mass 
[132].
4.3.5  Combined Methods
Combined methods training includes both specific sprint 
training (primary and or secondary methods) and tertiary 
methods, recommended by researchers and sprint and 
football code practitioners to develop sprint performance 
[24, 32, 133, 192–194]. This combination of both training 
methods enables practitioners to provide stimuli to develop 
both mechanical efficiency and the maximal physical capa-
bilities of the lower limb concurrently [110, 169, 170, 195]. 
Previous studies of combined specific and tertiary training 
methods demonstrated significant improvements in physi-
cal capacities (e.g., force, velocity, and power [36, 110]), 
increased stride lengths, reduced stride frequencies, and 
reduced stance contact times [76, 169, 170]. However, the 
changes in spatiotemporal variables are limited to short dis-
tances, with no significant changes presented in medium-dis-
tance sprints (e.g., stride length or frequency and contact or 
flight times [36, 76]). This review found significant within-
group moderate improvement at 0–30 m (SMD 0.43 [95% CI 
0.21–0.65; 95% PI − 0.69 to 1.55]) and small improvements 
in 0 to > 30 m (SMD 0.33 [95% CI 0.15–0.51; 95% PI − 0.50 
to 1.16]), with no significant change in the Vmax phase (SMD 
0.05 [95% CI − 0.23 to 0.34; 95% PI − 1.01 to 1.11]). Pair-
wise within-group analysis (sport only vs. combined) indi-
cated significant performance improvements in favour of 
combined methods (large SMD): 0 to > 30 m, SMD 1.51 
[95% CI 0.21–2.80; 95% PI − 4.26 to 7.28]). Interestingly, 
the 0–30 m and Vmax-phase contrasted with these findings, 
suggesting the combined methods were no more effective 
than sport-only training: 0–30 m (SMD 0.58 [95% CI − 0.93 
to 2.10; 95% PI − 5.20 to 6.37]); Vmax phase (SMD − 0.83 
[95% CI − 4.33 to 2.68; 95% PI not applicable as n < 3]). 
Sensitivity analysis appeared to indicate that the single study 
demonstrating a large reduction in the Vmax-phase sprint per-
formance changed both the statistical significance and the 
direction of the reported effect [97]. The negative effects 
reported in this study were attributed to the interference of 
the volume of aerobic training and thus is an important con-
sideration when attempting to develop medium to long sprint 
performance. As discussed in Sect. 4.3.3, phase-specific 
adaptations appear to be present, with performance changes 
likely a result of acceleration-specific adaptations reflected 
in short-sprint improvements included in the sprint outcome. 
Despite presenting significant training effects, each method 
presented different training methods (see Table S3 in the 
ESM). Therefore, combined specific methods appear to be 
an effective training method for football code athletes for 
0–30 and 0 to > 30 m sprint performance outcomes. How-
ever, if the aim is to develop the Vmax-phase performance, 
training strategies may be modified to develop phase-spe-
cific adaptations (e.g., increase vertical ground reaction in 
reduced stance phases). Further research is required to iden-
tify the optimal combination of exercises and training loads 
to improve phase-specific performance.
4.4  Moderator Variables
It is important to identify the moderator variables (i.e., foot-
ball code, sex, age, playing standard, stage of the season) 
that may impact upon sprint training outcomes. Studies were 
excluded from the analysis if the value used for subgroup 
analysis was not reported, if they did not provide sufficient 
277
Training Medium to Long Sprint Performance in Football Athletes
detail, or if they did not match the appropriate moderator 
categories.
4.4.1  Sex
The meta-analysis of the intervention training groups 
found that the sprint performance of both male and female 
football code athletes could be improved. However, the 
improvements for the 0 to > 30 m and Vmax-phase outcomes 
in females were not significant. When comparing male and 
female athletes, there was no significant difference between 
the training effects. This should be taken within the context 
of the scarcity of the available information on female athlete 
training compared with that for males [196]. The limited 
research comparing sex differences in training response in 
football code athletes found no significant effect of sex on 
changes in sprint performance [65]. Therefore, despite the 
demonstrated differences between physical characteristics 
[21, 132] and endocrine response [197] to training between 
males and females, evidence is currently insufficient to sug-
gest that practitioners should approach developing sprint 
performance differently based on an athlete’s sex.
4.4.2  Playing Standard
Both elite and sub-elite subgroups improved sprint perfor-
mance. However, there was no significant improvement 
in sub-elite Vmax-phase performance. The between-group 
comparison identified no significant difference between the 
training effects for elite and sub-elite groups. Despite sprint 
performance differentiating between performance standards 
[19–21], no study has explored whether sub-elite athletes are 
more sensitive to training than elite populations. However, 
research has demonstrated a decrement in the magnitude of 
the correlations with increasing levels of practice between 
the lower limb neuromuscular maximal capabilities and 
the ability to generate force during sprinting for sub-elite 
athletes compared with elite athletes [129, 150]. Therefore, 
further improvements may be represented by the ability 
to effectively apply force into the ground at progressively 
increasing velocities (mechanical effectiveness) to achieve 
either a greater rate of acceleration or enhanced Vmax perfor-
mance, or both. Hence, for medium- to long-distance sprints, 
a greater focus on developing the mechanical capabilities 
contributing to the athlete’ s ability to generate propulsive 
impulse (force × time) and their application at higher veloci-
ties and decreasing ground contact times (i.e., mechanical 
efficiency, theoretical maximal horizontal velocity and maxi-
mal power) is required [146, 150]. Theoretically, this may 
be achieved by using resisted sprints that enable athletes to 
apply force at high velocities (low loads or assisted sprint-
ing), by training at loads that correspond with optimal load 
for maximal power as well as low load (BM) or assisted 
horizontal or vertical jumps [146]. However, further research 
is required to demonstrate the effectiveness of these training 
strategies. Therefore, despite the demonstrated differences 
between physical characteristics between elite and sub-elite 
athletes [132] when considered independent of training 
status, evidence is insufficient to suggest that practitioners 
should approach developing sprint performance differently 
based on athlete’s playing standard within the football codes.
4.4.3  Age
The sprint performance of both senior and youth cohort 
subgroups was enhanced following training interventions, 
apart from the youth Vmax phase. Between-group compari-
sons identified that youth athletes enhanced sprint per-
formance more than senior athletes at 0 to > 30 m, which 
supports research stating that training response is typically 
greater in younger athletes than in their older counterparts 
[89]. Factors such as chronological age may have moder-
ated the training effects of the tertiary training methods in 
male youth athletes, with a greater training effect in younger 
(< 15 years) than in older (< 18 years) athletes [89]. Youth 
athletes experience multiple morphological and neural 
changes as a result of growth and maturation [198], which 
has implications for sprinting performance changes [48, 50, 
199]. The stage of maturation has been shown to moderate 
the training effect, with youth athletes training at pre-peak 
height velocity presenting lesser improvements than those 
at mid- and post-peak height velocity [48, 50]. Changes in 
youth cohorts may have been affected by the inclusion of 
pre-pubescent athletes and ineffective training exposures 
[93], which was not considered in the current analysis. These 
training effects suggest that coaches of youth athletes should 
take into consideration chronological and maturational age, 
increased baseline performance levels, and greater training 
experience [89, 200]. However, further research is required 
to understand sprint performance outcomes by age, which 
could include maturity grouping.
4.4.4  Sport
All sprint performance outcomes were improved in the 
soccer subgroup. Rugby union and American football pre-
sented significant improvements in 0–30 and 0 to > 30 m, 
respectively. No significant improvement was found in 
rugby league, rugby sevens, or Australian Rules football. 
Football codes training subgroups with limited representa-
tion in the literature (one to two training groups for a given 
distance outcome) were not considered for subgroup analy-
sis (e.g., 0–30 m rugby sevens [n = 1] [87]). Despite differ-
ences in physical characteristics [129, 132] and movement 
demands [158, 159], there were limited between-subgroup 
differences. The between-group comparison showed that 
278
 
B. Nicholson et al.
the increase in performance was significantly greater in 
soccer than in rugby union, rugby sevens, and American 
football (0 to > 30 m). The improvement in performance 
was significantly greater in American football than in rugby 
union (0 to > 30 m). No significant differences were found 
between the training effects for the football code subgroups 
for the Vmax-phase outcome. Although several factors may 
have contributed to the significant differences (e.g., training 
content, duration, frequency), the greater training experience 
in various forms of resistance training in the rugby codes 
and American football (e.g., ≥ 2 years’ systematic resistance 
training [76, 83, 105, 117, 118, 201]) may have resulted 
in lower morphological or neurological adaptability to the 
training stressors, resulting in lower training responses com-
pared with the less training-experienced soccer subgroups 
[130, 132]. However, the literature is insufficient to dem-
onstrate the between-subgroup differences across all sprint 
performance outcomes, and it remains unclear whether these 
are specific to training methods or distance outcomes. No 
study has compared the difference in training effects between 
football codes implementing matched training interventions 
in football code athletes on sprint performance. Therefore, 
evidence is insufficient to support coaches adapting sprint-
training methods based on football code.
4.4.5  Season
The in-season and off-season subgroups presented signifi-
cant improvements in 0–30 and 0 to > 30 m, despite prac-
titioners typically having less time available to develop 
physical or movement capacities during the in-season and 
off-season periods [51]. Pre-season subgroups only sig-
nificantly enhanced 0–30 m performance, and no signifi-
cant improvement in the Vmax phase was observed at any 
phase of the season. It is generally reported that fitness 
improvements are observed in the pre-season, with sub-
sequent stabilisation of such fitness variables in-season 
[202]. Consequently, greater benefits are expected in trials 
performed during the pre-season period than in those in 
the in-season [203, 204]. The between-group comparison 
found significantly greater improvements in-season com-
pared with pre-season and off-season in the 0 to > 30 m 
outcome only. Therefore, with appropriate prescribed 
training methods, 0–30 and 0 to > 30 m sprint performance 
can be enhanced in-season. The 0 to > 30 m pre-season 
subgroup was sensitive to the large reduction in training 
performance presented by Gabbett et al. [89], explain-
ing the lack of significant improvement. The Vmax phase 
appeared to present greater resistance to change based on 
the current training programmes. None of the included 
studies compared the difference between training effects 
between the phase of the season, implementing matched 
training interventions in football code athletes on sprint 
performance. Therefore, despite the differences in training 
demands between training phases, evidence is insufficient 
to support coaches adapting sprint-training methods based 
on the phase of the season.
4.5  Limitations
Whilst this work represents the largest systematic review 
and meta-analysis of medium- to long-distance sprint 
performance, limitations do exist. First, this review clas-
sified training into groups (i.e., sport-only, primary, sec-
ondary, tertiary, combined, and combined specific meth-
ods), which improved on previous classifications [32, 39] 
but also did not consider the complexity of sprint per-
formance development within the training prescription, 
the population, and the assessment methodologies. The 
broad within-group change approach taken was used to 
review all available literature; however, this method rep-
resents a suboptimal method of exploring training cau-
sality while also providing additional areas of bias to 
the interpretation (e.g., regression to the mean [205]). 
However, we attempted to address this by combining 
a within-group pre-post change design and a pairwise 
between-group design, enabling an evaluation of both 
high-quality controlled trial comparisons and an explora-
tion of the breadth of the available literature using a range 
of research designs. Despite the important influence of 
prior training status and physical capacities [128–130], it 
was not possible to include these as moderators for several 
reasons: (1) most studies did not report physical capacity 
and/or training experience within their descriptive statis-
tics; (2) those that did were inconsistent in how they were 
reported and the testing methods used; and (3) studies 
were often limited to years of football code-specific train-
ing or resistance training, with little consideration of how 
the stimulus was provided. Therefore, the level of detail 
to fully understand sprint development is lacking, but this 
is difficult in the context of understanding sprint develop-
ment and the multiple factors that interact. However, the 
review attempted to analyse several moderator variables 
(i.e., football code, sex, playing standard, age, and phase 
of the season), highlighting a limitation that most research 
is undertaken using parallel-group trials within male soc-
cer athletes involving mainly tertiary training methods. 
Therefore, research including randomised controlled trials 
across the football codes and female cohorts using multi-
ple training methods is limited, which may affect the meta-
analysis and moderator variable analysis and subsequent 
interpretation. Despite these limitations, the information 
gathered from the current review with meta-analysis may 
support practitioners in making evidence-informed deci-
sions when organising and evaluating training.
279
Training Medium to Long Sprint Performance in Football Athletes
4.6  Future Research Directions
This review presented similar research directions to those 
presented in the short-sprint training literature as the limi-
tations were consistent across all outcomes [25]. Where 
possible, future research should use high-quality research 
designs (e.g., randomised controlled trials) to expand and 
reaffirm the current findings whilst addressing the multiple 
gaps in specific populations. Research is required to exam-
ine the training effects in football codes other than soccer 
(e.g., rugby codes, American football, Australian Rules, 
Gaelic football, and futsal), in world-class and successful 
elite athletes, in trained populations with systematic training 
exposures, in youth athletes of various growth and matura-
tional status, and in female athlete cohorts. It is worth high-
lighting that, although several effective training methods 
are reported, it may be inappropriate to try to define the 
best methods for enhancing sprint performance in football 
(e.g., exercises, set and repetition schemes). Instead, the 
integration of different methods based on the training back-
ground, individual requirements, and progression over the 
training process needs to be further analysed to inform the 
optimal stimulus and organisation of training. It is essential 
that future research designs include pairing subjects based 
on resistance training experience and/or physical capacities 
(i.e., lower limb force characteristics) to establish a greater 
understanding of whether training changes and adaptations 
are dependent upon these variables. Both researchers and 
practitioners should consider the combined modelling of 
velocity–time curves with kinematic and kinetic changes 
assessed at more frequent intervals. This would enable prac-
titioners to isolate and confirm a time course of adaptations 
and the underlying causes of changes in performance [21, 
129, 150] whilst also reducing the limitations associated 
with pre- and post-sprint times or velocities [55]. Given the 
respective importance of repeated sprint ability and non-
linear sprint outcomes in the football codes, future research 
should explore their development, providing practitioners 
with a more comprehensive overview of developing athletes’ 
sprint characteristics.
Research identified that the majority of elite sprint and 
football code coaches reported utilising and advocating for 
an integrated approach using the combined training methods 
approach [24, 32, 133, 192–194]. This is performed both 
individually and in separate sessions and combinations (e.g., 
complex or contrast sets), enabling the development of mul-
tiple physical capacities and skills simultaneously [24, 32, 
133, 192–194]. Therefore, further research would be better 
suited to manipulating the combination, sequencing, and 
loading parameters of combined specific and non-specific 
methods to enhance sprint performance longitudinally as, 
ultimately within the football codes, combined training is 
implemented. This should be combined with methods of 
profiling that allow optimisation and individualisation of 
training exposures [150, 189, 206–208], which may reduce 
the variability in performance change [189]. While exercise 
specificity is certainly an important principle when develop-
ing a training programme, it is only one of several princi-
ples that will influence the effectiveness of the programme. 
Therefore, future research should continue to explore within 
and between subgroups the effects of overload, variation, 
and reversibility and the effect on sprint performance change 
[26]. Furthermore, this needs to be supported with determin-
ing the minimal and optimal training doses to retain and 
develop sprint performance in football code athletes. This 
will directly influence practitioners’ organisation of training 
and the prescribed loading variables.
5  Conclusions
Establishing the most effective methods to improve medium- 
to long-distance performance outcomes is an important 
consideration for practitioners working across the football 
codes. This work represents the first systematic review and 
meta-analysis of sprint performance development using 
medium- to long-distance outcomes that include all training 
modalities while exclusively assessing within- and across-
football code athletes. The results indicate that medium to 
long sprint performance outcomes can be enhanced through 
secondary (i.e., resisted or assisted sprinting), combined 
(i.e., primary or secondary and tertiary training methods) 
(0–30 m and 0 to > 30 m), and tertiary training methods 
(0–30 m). In addition, tertiary training methods were the 
only method that enhanced Vmax-phase performance signifi-
cantly. Performance changes in outcomes > 20 m may be 
attributed to either or both adaptations specific to the accel-
eration or Vmax phases, and not Vmax exclusively. Despite 
this, when comparing training typology, no individual mode 
was found to be the most effective. However, both sport-
only training and primary training methods appeared to 
be insufficient to develop medium- to long-distance sprint 
performance outcomes. The null and negative performance 
effects present in all training group PIs warrant caution, as—
regardless of training mode-specific point estimate—factors 
such as athlete’s capacities, previous training exposures, and 
the programme design may moderate positive performance 
adaptations. Moderator effects, although not mode specific, 
suggested that there is no consistent effect of age, sex, play-
ing standard, and phase of the season on sprint performance 
change across outcomes. Regardless of the population char-
acteristics, medium- to long-distance sprint performance can 
be enhanced by increasing either the magnitude or the ori-
entation of force an athlete can generate and express in the 
sprinting action, or both. These findings present practitioners 
280
 
B. Nicholson et al.
with several options to suit their programme to enhance 
medium- to long-distance sprint performance.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s40279- 021- 01552-4.
Declarations 
Funding No sources of funding were used to assist in the preparation 
of this article.
Conflict of interest Ben Nicholson, Alex Dinsdale, Ben Jones, and 
Kevin Till have no conflicts of interest that are directly relevant to the 
content of this article.
Availability of data and materials The datasets generated during and/or 
analysed during the current study are available from the corresponding 
author on reasonable request.
Ethics approval Approval was obtained from the ethics committee of 
Leeds Beckett University. The procedures used in this study comply 
with the ethical standards of the Declaration of Helsinki.
Consent for publication Not applicable.
Author contributions All the authors contributed to the manuscript, 
including the conception and design of the study, analysis and inter-
pretation of the data, drafting and critical revision of the manuscript, 
and approval for publication. All authors read and approved the final 
manuscript.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long 
as you give appropriate credit to the original author(s) and the source, 
provide a link to the Creative Commons licence, and indicate if changes 
were made. The images or other third party material in this article are 
included in the article's Creative Commons licence, unless indicated 
otherwise in a credit line to the material. If material is not included in 
the article's Creative Commons licence and your intended use is not 
permitted by statutory regulation or exceeds the permitted use, you will 
need to obtain permission directly from the copyright holder. To view a 
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
References
 1. Deutsch M, Kearney G, Rehrer N. Time–motion analysis of pro-
fessional rugby union players during match-play. J Sport Sci. 
2007;25(4):461–72.
 2. Barnes C, Archer D, Hogg B, Bush M, Bradley P. The evolution 
of physical and technical performance parameters in the English 
Premier League. Int J Sport Med. 2014;35(13):1095–100.
 3. Johnston RD, Gabbett TJ, Jenkins DG. Applied sport science of 
rugby league. Sport Med. 2014;44(8):1087–100.
 4. Veale JP, Pearce AJ, Carlson JS. Player movement patterns in an 
elite junior Australian Rules Football Team: an exploratory study. 
J Sport Sci Med. 2007;6(2):254.
 5. NFL. Top plays: Fastest ball carriers https:// nextg ensta ts. nfl. com/ 
stats/ top- plays/ faste st- ball- carri ers/ 2018/ all: NFL next gen stats; 
2018.
 6. Duthie GM, Pyne DB, Marsh DJ, Hooper SL. Sprint patterns in 
rugby union players during competition. J Strength Cond Res. 
2006;20(1):208–14.
 7. Benton D. Sprint running needs of field sport athletes: a new 
perspective. Sports Coach. 2001;24(2):12–4.
 8. Clark KP, Rieger RH, Bruno RF, Stearne DJ. The national foot-
ball league combine 40-yd dash: how important is maximum 
velocity? J Strength Cond Res. 2019;33(6):1542–50.
 9. Barr MJ, Sheppard JM, Newton RU. Sprinting kinematics of elite 
rugby players. J Aust Strength Cond. 2013;21(4):14–20.
 10. Young WB, Duthie GM, James LP, Talpey SW, Benton DT, 
Kilfoyle A. Gradual vs. maximal acceleration: their influence 
on the prescription of maximal speed sprinting in team sport 
athletes. Sports. 2018;6(3):66.
 11. Gabbett TG. Sprinting patterns of national rugby league com-
petition. J Strength Cond Res. 2012;26(1):121–30.
 12. Suarez-Arrones L, Arenas C, López G, Requena B, Terrill O, 
Mendez-Villanueva A. Positional differences in match running 
performance and physical collisions in men rugby sevens. Int 
J Sports Physiol Perform. 2014;9(2):316–23.
 13. Di Salvo V, Baron R, González-Haro C, Gormasz C, Pigozzi 
F, Bachl N. Sprinting analysis of elite soccer players during 
European Champions League and UEFA Cup matches. J Sport 
Sci. 2010;28(14):1489–94.
 14. Di Salvo V, Baron R, Tschan H, Montero FC, Bachl N, Pigozzi 
F. Performance characteristics according to playing position in 
elite soccer. Int J Sport Med. 2007;28(03):222–7.
 15. Bishop DJ, Girard O. Determinants of team-sport performance: 
implications for altitude training by team-sport athletes. Br J 
Sports Med. 2013;47:i17–21.
 16. Ross A, Gill N, Cronin J, Malcata R. The relationship between 
physical characteristics and match performance in rugby sev-
ens. Eur J Sports Sci. 2015;15(6):565–71.
 17. Gabbett TJ, Jenkins DG, Abernethy B. Relationships between 
physiological, anthropometric, and skill qualities and playing 
performance in professional rugby league players. J Sport Sci. 
2011;29(15):1655–64.
 18. Gabbett T, Kelly J, Pezet T. Relationship between physical 
fitness and playing ability in rugby league players. J Strength 
Cond Res. 2007;21(4):1126.
 19. Till K, Scantlebury S, Jones B. Anthropometric and physical 
qualities of elite male youth rugby league players. Sport Med. 
2017;47(11):2171–86.
 20. Delaney JA, Olson TM, Morin J-B. Sprint acceleration 
mechanical profiling for the NFL draft. Sports Perform Sci 
Rep. 2018;27:1–3.
 21. Baumgart C, Freiwald J, Hoppe M. Sprint mechanical proper-
ties of female and different aged male top-level german soccer 
players. Sports. 2018;6(4):161.
 22. Al Haddad H, Simpson BM, Buchheit M, Di Salvo V, Mendez-
Villanueva A. Peak match speed and maximal sprinting speed 
in young soccer players: effect of age and playing position. Int 
J Sports Physiol Perform. 2015;10(7):888–96.
 23. Faude O, Koch T, Meyer T. Straight sprinting is the most fre-
quent action in goal situations in professional football. J Sport 
Sci. 2012;30(7):625–31.
 24. Jones TW, Smith A, Macnaughton LS, French DN. Strength 
and conditioning and concurrent training practices in elite 
rugby union. J Strength Cond Res. 2016;30(12):3354–66.
 25. Nicholson B, Dinsdale A, Jones B, Till K. The training of 
short distance sprint performance in football code ath-
letes: a systematic review and meta-analysis. Sport Med. 
2020;Forthcoming:1–29.
 26. Moir GL, Brimmer SM, Snyder BW, Connaboy C, Lamont 
HS. Mechanical limitations to sprinting and biomechanical 
solutions: a constraints-led framework for the incorporation of 
281
Training Medium to Long Sprint Performance in Football Athletes
resistance training to develop sprinting speed. Strength Cond J. 
2018;40(1):47–67.
 27. Colyer SL, Nagahara R, Takai Y, Salo AI. How sprinters accel-
erate beyond the velocity plateau of soccer players: Waveform 
analysis of ground reaction forces. Scand J Med Sci Sports. 
2018;28(12):2527–35.
 28. Wild JJ, Bezodis IN, North JS, Bezodis NE. Differences in step 
characteristics and linear kinematics between rugby players and 
sprinters during initial sprint acceleration. Eur J Sports Sci. 
2018;18(10):1327–37.
 29. Buchheit M, Samozino P, Glynn JA, Michael BS, Al Haddad H, 
Mendez-Villanueva A, et al. Mechanical determinants of accel-
eration and maximal sprinting speed in highly trained young soc-
cer players. J Sport Sci. 2014;32(20):1906–13.
 30. Sides D. Kinematics and kinetics of maximal velocity sprinting 
and specificity of training in elite athletes. Salford: University of 
Salford; 2015.
 31. Bissas A, Walker J, Tucker C, Paradisis G, Merlino S. Biome-
chanical report for the IAAF World Championships London 
2017: 100 m men’s. Monte Carlo: IAAF. 2018.
 32. Rumpf MC, Lockie RG, Cronin JB, Jalilvand F. Effect of different 
sprint training methods on sprint performance over various dis-
tances: a brief review. J Strength Cond Res. 2016;30(6):1767–85.
 33. Alcaraz PE, Carlos-Vivas J, Oponjuru BO, Martínez-Rodríguez 
A. The effectiveness of resisted sled training (RST) for sprint 
performance: a systematic review and meta-analysis. Sport Med. 
2018;48(9):2143–65.
 34. Douglas J, Pearson S, Ross A, McGuigan M. Effects of accentu-
ated eccentric loading on muscle properties, strength, power, and 
speed in resistance-trained rugby players. J Strength Cond Res. 
2018;32(10):2750–61.
 35. Enoksen E, Staxrud M, Tønnessen E, Shalfawi S. The effect of 
supervised strength training on young elite male soccer players’ 
physical performance. Serbian J Sport Sci. 2013;7(4):173–9.
 36. Lahti J, Huuhka T, Romero V, Bezodis I, Morin J, Hakkinen 
K. Changes in sprint performance and sagittal plane kinematics 
after heavy resisted sprint training in professional soccer players 
[Pre-print]. 2019. https:// osf. io/ prepr ints/ sport rxiv/ neh5m/ 2019 
Updated 27 Aug 2020. 
 37. Loturco I, Nakamura F, Kobal R, Gil S, Pivetti B, Pereira L, et al. 
Traditional periodization versus optimum training load applied 
to soccer players: effects on neuromuscular abilities. Int J Sport 
Med. 2016;37(13):1051–9.
 38. Spinks CD, Murphy AJ, Spinks WL, Lockie RG. The effects 
of resisted sprint training on acceleration performance and kin-
ematics in soccer, rugby union, and Australian football players. 
J Strength Cond Res. 2007;21(1):77–85.
 39. Plisk S (2000) Speed, agility, and speed-endurance development. 
In: Baechle TR, Earle RW. (eds.) Human Kinetics, 2nd edition. 
Champaign IL, p 471–91.
 40. Bauer P, Uebellacker F, Mitter B, Aigner AJ, Hasenoehrl T, 
Ristl R, et al. Combining higher-load and lower-load resist-
ance training exercises: a systematic review and meta-analysis 
of findings from complex training studies. J Sci Med Sport. 
2019;22(7):838–51.
 41. García-Ramos A, Haff GG, Feriche B, Jaric S. Effects of different 
conditioning programmes on the performance of high-velocity 
soccer-related tasks: systematic review and meta-analysis of con-
trolled trials. Int J Sports Sci Coach. 2018;13(1):129–51.
 42. Freitas TT, Martinez-Rodriguez A, Calleja-González J, Alcaraz 
PE. Short-term adaptations following complex training in team-
sports: a meta-analysis. PLoS ONE. 2017;12(6):180223.
 43. Moran JJ, Sandercock GR, Ramírez-Campillo R, Meylan 
CM, Collison JA, Parry DA. Age-related variation in male 
youth athletes’ countermovement jump after plyometric 
training: a meta-analysis of controlled trials. J Strength Cond 
Res. 2017;31(2):552–65.
 44. Petrakos G, Morin J-B, Egan B. Resisted sled sprint training to 
improve sprint performance: a systematic review. Sport Med. 
2016;46(3):381–400.
 45. Bolger R, Lyons M, Harrison AJ, Kenny IC. Sprinting perfor-
mance and resistance-based training interventions: a systematissc 
review. J Strength Cond Res. 2015;29(4):1146–56.
 46. Hrysomallis C. The effectiveness of resisted movement training 
on sprinting and jumping performance. J Strength Cond Res. 
2012;26(1):299–306.
 47. Harries SK, Lubans DR, Callister R. Resistance training to 
improve power and sports performance in adolescent ath-
letes: a systematic review and meta-analysis. J Sci Med Sport. 
2012;15(6):532–40.
 48. Rumpf MC, Cronin JB, Pinder SD, Oliver J, Hughes M. Effect of 
different training methods on running sprint times in male youth. 
Pediatr Exerc Sci. 2012;24(2):170–86.
 49. de Villarreal ES, Requena B, Cronin JB. The effects of plyomet-
ric training on sprint performance: a meta-analysis. J Strength 
Cond Res. 2012;26(2):575–84.
 50. Moran J, Sandercock G, Rumpf MC, Parry DA. Variation in 
responses to sprint training in male youth athletes: a meta-anal-
ysis. Int J Sport Med. 2017;38(01):1–11.
 51. Haugen T. Sprint conditioning of elite soccer players: worth 
the effort or lets just buy faster players? Sport Perform Sci Rep. 
2017;1:1–2.
 52. Ryan R. Heterogeneity and subgroup analyses in Cochrane Con-
sumers and Communication Review Group reviews: planning the 
analysis at protocol stage. Cochrane Consumers Communication 
Review Group. 2014.
 53. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, 
Ioannidis JP, et al. The PRISMA statement for reporting sys-
tematic reviews and meta-analyses of studies that evaluate health 
care interventions: explanation and elaboration. PLoS Med. 
2009;6(7):e1000100.
 54. McMaster DT, Gill N, Cronin J, McGuigan M. The develop-
ment, retention and decay rates of strength and power in elite 
rugby union, rugby league and American football. Sport Med. 
2013;43(5):367–84.
 55. Haugen TA, Breitschädel F, Samozino P. Power-force-velocity 
profiling of sprinting athletes: methodological and practical 
considerations when using timing gates. J Strength Cond Res. 
2018;34(6):1769–73.
 56. Haugen T, Buchheit M. Sprint running performance monitor-
ing: methodological and practical considerations. Sport Med. 
2016;46(5):641–56.
 57. Gurevitch J, Koricheva J, Nakagawa S, Stewart G. Meta-
analysis and the science of research synthesis. Nature. 
2018;555(7695):175.
 58. Bremec D. Very heavy resisted sprinting: a better way to improve 
acceleration? Effects of a 4-week very heavy resisted sprinting 
intervention on acceleration, sprint and jump performance in 
youth soccer players [Masters Thesis]. Swedish School of Sport 
and Health Sciences; 2018.
 59. Coratella G, Beato M, Milanese C, Longo S, Limonta E, Rampi-
chini S, et al. Specific adaptations in performance and muscle 
architecture after weighted jump-squat vs body mass squat jump 
training in recreational soccer players. J Strength Cond Res. 
2019;32(4):921–9.
 60. Derakhti M. Very heavy resisted sprint training for adolescent 
football players: a training intervention on acceleration, sprint 
and jump performance in late pubertal adolescent athletes [Mas-
ters of sports science]. Swedish School of sport and health sci-
ence, 2018.
282
 
B. Nicholson et al.
 61. Hammami M, Gaamouri N, Shephard RJ, Chelly MS. Effects 
of contrast strength vs plyometric training on lower limb 
explosive performance, ability to change direction and neuro-
muscular adaptation in soccer players. J Strength Cond Res. 
2018;33(8):2094–103.
 62. Manouras N, Papanikolaou Z, Karatrantou K, Kouvarakis P, 
Gerodimos V. The efficacy of vertical vs horizontal plyometric 
training on speed, jumping performance and agility in soccer 
players. Int J Sport Sci Coach. 2016;11(5):702–9.
 63. Ramírez-Campillo R, Burgos CH, Henríquez-Olguín C, Andrade 
DC, Martínez C, Álvarez C, et al. Effect of unilateral, bilateral, 
and combined plyometric training on explosive and endurance 
performance of young soccer players. J Strength Cond Res. 
2015;29(5):1317–28.
 64. Ramírez-Campillo R, Gallardo F, Henriquez-Olguín C, Meylan 
CM, Martínez C, Álvarez C, et al. Effect of vertical, horizontal, 
and combined plyometric training on explosive, balance, and 
endurance performance of young soccer players. J Strength Cond 
Res. 2015;29(7):1784–95.
 65. Ramírez-Campillo R, Vergara-Pedreros M, Henríquez-Olguín 
C, Martínez-Salazar C, Alvarez C, Nakamura FY, et  al. 
Effects of plyometric training on maximal-intensity exercise 
and endurance in male and female soccer players. J Sport Sci. 
2016;34(8):687–93.
 66. Ramirez-Campillo R, Álvarez C, García-Pinillos F, García-
Ramos A, Loturco I, Chaabene H, et al. Effects of combined 
surfaces vs. single-surface plyometric training on soccer players' 
physical fitness. J Strength Cond Res. 2019. (Publish Ahead of 
Print).
 67. Rimmer E, Sleivert G. Effects of a plyometrics interven-
tion program on sprint performance. J Strength Cond Res. 
2000;14(3):295–301.
 68. Rønnestad BR, Kvamme NH, Sunde A, Raastad T. Short-term 
effects of strength and plyometric training on sprint and jump 
performance in professional soccer players. J Strength Cond Res. 
2008;22(3):773–80.
 69. Higgins JP, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, 
et al. Cochrane handbook for systematic reviews of interventions. 
Hoboken: John Wiley & Sons; 2019.
 70. Ades AE, Lu G, Higgins JPT. The interpretation of ran-
dom-effects meta-analysis in decision models. MDM. 
2005;25(6):646–54.
 71. Hopkins W, Marshall S, Batterham A, Hanin J. Progressive sta-
tistics for studies in sports medicine and exercise science. Med 
Sci Sports Exerc. 2009;41(1):3.
 72. Higgins J, Thompson SG. Quantifying heterogeneity in a meta-
analysis. Stat Med. 2002;21(11):1539–58.
 73. Swann C, Moran A, Piggott D. Defining elite athletes: Issues in 
the study of expert performance in sport psychology. Psychol 
Sport Exerc. 2015;16:3–14.
 74. Sterne JA, Egger M, Moher D. Addressing reporting biases. 
Cochrane handbook for systematic reviews of interventions: 
Cochrane book series. 2008:297–333.
 75. Alptekin A, Kılıç Ö, Maviş M. The effect of an 8-week plyomet-
ric training program on sprint and jumping performance. Serbian 
J Sport Sci. 2013;7(2):45–50.
 76. Barr MJ, Gabbett TJ, Newton RU, Sheppard JM. Effect of 8 
days of a hypergravity condition on the sprinting speed and 
lower-body power of elite rugby players. J Strength Cond Res. 
2015;29(3):722–9.
 77. Beato M, Bianchi M, Coratella G, Merlini M, Drust B. Effects 
of plyometric and directional training on speed and jump per-
formance in elite youth soccer players. J Strength Cond Res. 
2018;32(2):289–96.
 78. Bianchi M, Coratella G, Dello IA, Beato M. Comparative effects 
of single vs double weekly plyometric training sessions on jump, 
sprint and change of directions abilities of elite youth football 
players. J Sports Med Phys Fit. 2019;59(6):910.
 79. Borges JH, Conceição MS, Vechin FC, Pascoal EHF, Silva RP, 
Borin JP. The effects of resisted sprint vs plyometric training on 
sprint performance and repeated sprint ability during the final 
weeks of the youth soccer season. Sci Sports. 2016;31(4):101–5.
 80. Bouguezzi R, Chaabene H, Negra Y, Ramirez-Campillo R, Jlalia 
Z, Mkaouer B, et al. Effects of different plyometric training fre-
quency on measures of athletic performance in prepuberal male 
soccer players. J Strength Cond Res. 2020;34(6):1609–17.
 81. Chelly MS, Ghenem MA, Abid K, Hermassi S, Tabka Z, 
Shephard RJ. Effects of in-season short-term plyometric training 
program on leg power, jump-and sprint performance of soccer 
players. J Strength Cond Res. 2010;24(10):2670–6.
 82. Christou M, Smilios I, Sotiropoulos K, Volaklis K, Pilianidis 
T, Tokmakidis SP. Effects of resistance training on the physical 
capacities of adolescent soccer players. J Strength Cond Res. 
2006;20(4):783–91.
 83. Cook CJ, Beaven CM, Kilduff LP. Three weeks of eccentric 
training combined with overspeed exercises enhances power 
and running speed performance gains in trained athletes. J 
Strength Cond Res. 2013;27(5):1280–6.
 84. Coutts A, Reaburn P, Piva TJ, Murphy A. Changes in selected 
biochemical, muscular strength, power, and endurance meas-
ures during deliberate overreaching and tapering in rugby 
league players. Int J Sport Med. 2007;28(2):116–24.
 85. de Hoyo M, Gonzalo-Skok O, Sañudo B, Carrascal C, Plaza-
Armas JR, Camacho-Candil F, et al. Comparative effects of in-
season full-back squat, resisted sprint training, and plyometric 
training on explosive performance in U-19 elite soccer players. 
J Strength Cond Res. 2016;30(2):368–77.
 86. Escobar-Álvarez J, Fuentes-García J, Pérez-Sousa M, Calleja-
González J. Effect of very heavy sled training in speed per-
formance (30m) in female rugby union players. In: ICTS VI 
International Conference in Team Sports; España2018. p 1.
 87. Escobar-Alvarez J, Pérez-Sousa MA, Fuentes J, Jimenez-Reyes 
P. Effect of velocity-based training on strength, CMJ height and 
sprint performance in professional rugby 7 players. I Congreso 
Internacional de Innovación en el Deporte; Cáceres, Spain. 
2019.
 88. Faude O, Roth R, Di Giovine D, Zahner L, Donath L. Combined 
strength and power training in high-level amateur football during 
the competitive season: a randomised-controlled trial. J Sport 
Sci. 2013;31(13):1460–7.
 89. Gabbett TJ, Johns J, Riemann M. Performance changes follow-
ing training in junior rugby league players. J Strength Cond Res. 
2008;22(3):910–7.
 90. García-Pinillos F, Martínez-Amat A, Hita-Contreras F, Martínez-
López EJ, Latorre-Román PA. Effects of a contrast training pro-
gram without external load on vertical jump, kicking speed, 
sprint, and agility of young soccer players. J Strength Cond Res. 
2014;28(9):2452–60.
 91. Gil S, Barroso R, Crivoi do Carmo E, Loturco I, Kobal R, Tricoli 
V, et al. Effects of resisted sprint training on sprinting ability and 
change of direction speed in professional soccer players. J Sport 
Sci. 2018;36(17):1923–9.
 92. Hammami M, Negra Y, Aouadi R, Shephard RJ, Chelly MS. 
Effects of an in-season plyometric training program on repeated 
change of direction and sprint performance in the junior soccer 
player. J Strength Cond Res. 2016;30(12):3312–20.
 93. Hammami R, Granacher URS, Makhlouf I, Behm DG, Chaouachi 
A. Sequencing effects of balance and plyometric training on 
physical performance in youth soccer athletes. J Strength Cond 
Res. 2016;30(12):3278–89.
283
Training Medium to Long Sprint Performance in Football Athletes
 94. Harris NK, Cronin JB, Hopkins WG, Hansen KT. Squat jump 
training at maximal power loads vs heavy loads: effect on sprint 
ability. J Strength Cond Res. 2008;22(6):1742–9.
 95. Karsten B, Larumbe-Zabala E, Kandemir G, Hazir T, Klose A, 
Naclerio F. The effects of a 6-week strength training on criti-
cal velocity, anaerobic running distance, 30-m sprint and Yo-Yo 
intermittent running test performances in male soccer players. 
PLoS ONE. 2016;11(3):e0151448.
 96. Krommes K, Petersen J, Nielsen MB, Aagaard P, Hölmich P, 
Thorborg K. Sprint and jump performance in elite male soccer 
players following a 10-week Nordic Hamstring exercise protocol: 
a randomised pilot study. BMC Res Notes. 2017;10(1):669.
 97. López-Segovia M, Andrés JMP, González-Badillo JJ. Effect 
of 4 months of training on aerobic power, strength, and accel-
eration in two under-19 soccer teams. J Strength Cond Res. 
2010;24(10):2705–14.
 98. Loturco I, Ugrinowitsch C, Tricoli V, Pivetti B, Roschel H. Dif-
ferent loading schemes in power training during the preseason 
promote similar performance improvements in brazilian elite 
soccer players. J Strength Cond Res. 2013;27(7):1791–7.
 99. Loturco I, Pereira LA, Kobal R, Maldonado T, Piazzi AF, Bot-
tino A, et al. Improving sprint performance in soccer: effective-
ness of jump squat and olympic push press exercises. PLoS 
ONE. 2016;11(4):153958.
 100. Loturco I, Kobal R, Kitamura K, Cal Abad CC, Faust B, 
Almeida L, et al. Mixed training methods: effects of combining 
resisted sprints or plyometrics with optimum power loads on 
sprint and agility performance in professional soccer players. 
Front Physiol. 2017;8:1034.
 101. Majdell R, Alexander M. The effect of overspeed train-
ing on kinematic variables in sprinting. J Hum Mov Stud. 
1991;21(1):19–39.
 102. Meckel Y, Gefen Y, Nemet D, Eliakim A. Influence of 
short vs long repetition sprint training on selected fitness 
components in young soccer players. J Strength Cond Res. 
2012;26(7):1845–51.
 103. Michailidis Y, Tabouris A, Metaxas T. Effects of plyometric and 
directional training on physical fitness parameters in youth soccer 
players. Int J Sports Physiol Perform. 2019;14(3):392–8.
 104. Negra Y, Chaabene H, Hammami M, Hachana Y, Granacher 
URS. Effects of high-velocity resistance training on athletic per-
formance in prepuberal male soccer athletes. J Strength Cond 
Res. 2016;30(12):3290–7.
 105. Orange ST, Metcalfe JW, Robinson A, Applegarth MJ, Liefeith 
A. Effects of in-season velocity-versus percentage-based training 
in academy rugby league players. Int J Sports Physiol Perform. 
2019;1(2):1–8.
 106. Ozbar N. Effects of plyometric training on explosive strength, 
speed and kicking speed in female soccer players. Anthropol. 
2015;19(2):333–9.
 107. Randell AD, Cronin JB, Keogh JWL, Gill ND, Pedersen MC. 
Effect of instantaneous performance feedback during 6 weeks of 
velocity-based resistance training on sport-specific performance 
tests. J Strength Cond Res. 2011;25(1):87–93.
 108. Rey E, PadrÓN-Cabo A, FernÁNdez-Penedo D. Effects of sprint 
training with and without weighted vest on speed and repeated 
sprint ability in male soccer players. J Strength Cond Res. 
2017;31(10):2659–66.
 109. Rønnestad BR, Nymark BS, Raastad T. Effects of in-season 
strength maintainance training frequency in professional scocer 
players. J Strength Cond Res. 2011;25(10):2653–60.
 110. Ross A, Gill N, Cronin J, Cross M. The effects of two power 
training programmes on the sprint speed, mechanical sprint char-
acteristics, and lower body power of rugby sevens players [Doc-
tor of Philosophy (PhD)]. New Zealand: Auckland University of 
Technology; 2015.
 111. Scott BR, Peiffer JJ, Goods PSR. The effects of supplementary 
low-load blood flow restriction training on morphological and 
performance-based adaptations in team sport athletes. J Strength 
Cond Res. 2017;31(8):2147–54.
 112. Shalfawi SA, Ingebrigtsen J, Dillern T, Tønnessen E, Delp TK, 
Enoksen E. The effect of 40 m repeated sprint training on physi-
cal performance in young elite male soccer players. Serbian J 
Sport Sci. 2012;6(3).
 113. Söhnlein Q, Müller E, Stöggl TL. The effect of 16-week plyo-
metric training on explosive actions in early to mid-puberty elite 
soccer players. J Strength Cond Res. 2014;28(8):2105–14.
 114. Tønnessen E, Shalfawi SA, Haugen T, Enoksen E. The effect 
of 40-m repeated sprint training on maximum sprinting speed, 
repeated sprint speed endurance, vertical jump, and aerobic 
capacity in young elite male soccer players. J Strength Cond 
Res. 2011;25(9):2364–70.
 115. Tous-Fajardo J, Gonzalo-Skok O, Arjol-Serrano JL, Tesch 
P. Enhancing change-of-direction speed in soccer players by 
functional inertial eccentric overload and vibration training. 
Int J Sports Physiol Perform. 2016;11(1):66–73.
 116. Upton DE. The effect of assisted and resisted sprint training on 
acceleration and velocity in Division IA female soccer athletes. 
J Strength Cond Res. 2011;25(10):2645–52.
 117. West DJ, Cunningham DJ, Bracken RM, Bevan HR, Crewther 
BT, Cook CJ, et al. Effects of resisted sprint training on accel-
eration in professional rugby union players. J Strength Cond 
Res. 2013;27(4):1014–8.
 118. Winwood PW, Cronin JB, Posthumus LR, Finlayson SJ, Gill 
ND, Keogh JW. Strongman vs. traditional resistance training 
effects on muscular function and performance. J Strength Cond 
Res. 2015;29(2):429–39.
 119. Wong P, Chaouachi A, Chamari K, Dellal A, Wisloff U. Effect 
of preseason concurrent muscular strength and high-intensity 
interval training in professional soccer players. J Strength Cond 
Res. 2010;24(3):653–60.
 120. McMaster D, Gill N, McGuigan M, Cronin J. Effects of com-
plex strength and ballistic training on maximum strength, sprint 
ability and force-velocity-power profiles of semi-professional 
rugby union players. J Aust Strength Cond. 2014;22(1):17–30.
 121. Sterne JA, Gavaghan D, Egger M. Publication and related bias 
in meta-analysis: power of statistical tests and prevalence in 
the literature. J Clin Epidemiol. 2000;53(11):1119–29.
 122. Harris GR, Stone MH, Obryant HS, Proulx CM, Johnson RL. 
Short-term performance effects of high power, high force, 
or combined weight-training methods. J Strength Cond Res. 
2000;14(1):14–20.
 123. Hoffman JR, Cooper J, Wendell M, Kang J. Comparison of 
olympic vs traditional power lifting training programs in foot-
ball players. J Strength Cond Res. 2004;18(1):129–35.
 124. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring 
inconsistency in meta-analyses. BMJ. 2003;327(7414):557–60.
 125. Kobal R, Pereira LA, Zanetti V, Ramirez-Campillo R, Loturco 
I. Effects of unloaded vs loaded plyometrics on speed and 
power performance of elite young soccer players. Front Phys-
iol. 2017;8:742.
 126. Hoffmann TC, Glasziou PP, Boutron I, Milne R, Perera R, 
Moher D, et al. Better reporting of interventions: template for 
intervention description and replication (TIDieR) checklist and 
guide. BMJ. 2014;348:g1687.
 127. Cuthbert M, Ripley N, McMahon JJ, Evans M, Haff GG, Com-
fort P. The effect of Nordic hamstring exercise intervention 
volume on eccentric strength and muscle architecture adap-
tations: a systematic review and meta-analyses. Sport Med. 
2019;50(1):83–99.
284
 
B. Nicholson et al.
 128. Medicine ACoS. American College of Sports Medicine posi-
tion stand progression models in resistance training for healthy 
adults. Med Sci Sports Exerc. 2009;41(3):687.
 129. Jimenez-Reyes P, Samozino P, García Ramos A, Cuadrado V, 
Brughelli M, Morin J-B. Relationship between vertical and 
horizontal force-velocity-power profiles in various sports and 
levels of practice. PeerJ. 2018;6:e5937.
 130. Haugen T, Seiler S, Sandbakk O, Tønnessen E. The training 
and development of elite sprint performance: an integration 
of scientific and best practice literature. Sport Med Open. 
2019;5:1–6.
 131. Sale D, MacDougall D. Specificity in strength training: a review 
for the coach and athlete. Can J Appl Sport Sci. 1981;6(2):87–92.
 132. Haugen TA, Breitschädel F, Seiler S. Sprint mechanical vari-
ables in elite athletes: are force-velocity profiles sport specific 
or individual? Int J Sports Physiol. 2019;14(7):215551.
 133. Brearley S, Bishop C. Transfer of training: how specific should 
we be? Strength Cond J. 2019;41(3):97–109.
 134. Kanehisa H, Matsuo A, Fukunaga T. Are peak ground reaction 
forces related to better sprint acceleration performance? Sports 
Biomech. 2019:1–10.
 135. Nagahara R, Takai Y, Kanehisa H, Fukunaga T. Vertical 
impulse as a determinant of combination of step length and fre-
quency during sprinting. Int J Sport Med. 2018;39(4):282–90.
 136. Weyand PG, Sternlight DB, Bellizzi MJ, Wright S. Faster top 
running speeds are achieved with greater ground forces not 
more rapid leg movements. J Appl Physiol. 2000;89(5):1991–9.
 137. Weyand PG, Sandell RF, Prime DN, Bundle MW. The biologi-
cal limits to running speed are imposed from the ground up. J 
Appl Physiol. 2010;108(4):950–61.
 138. Morin J-B, Edouard P, Samozino P. Technical ability of force 
application as a determinant factor of sprint performance. Med 
Sci Sports Exerc. 2011;43(9):1680–8.
 139. Morin J-B, Slawinski J, Dorel S, Couturier A, Samozino P, 
Brughelli M, et al. Acceleration capability in elite sprint-
ers and ground impulse: push more, brake less? J Biomech. 
2015;48(12):3149–54.
 140. Morin J-B, Bourdin M, Edouard P, Peyrot N, Samozino P, 
Lacour J-R. Mechanical determinants of 100-m sprint running 
performance. Eur J Appl Physiol. 2012;112(11):3921–30.
 141. Rabita G, Dorel S, Slawinski J, Sàez-de-Villarreal E, Couturier 
A, Samozino P, et al. Sprint mechanics in world-class athletes: 
a new insight into the limits of human locomotion. Scand J 
Med Sci Sports. 2015;25(5):583–94.
 142. Morin J-B, Gimenez P, Edouard P, Arnal P, Jiménez-Reyes P, 
Samozino P, et al. Sprint acceleration mechanics: the major 
role of hamstrings in horizontal force production. Front Phys-
iol. 2015;6:404.
 143. Clark K, Weyand P. Sprint running research speeds up: a first 
look at the mechanics of elite acceleration. Scand J Med Sci 
Sports. 2015;25(5):581–2.
 144. Bezodis IN, Kerwin DG, Cooper S-M, Salo AI. Sprint running 
performance and technique changes in athletes during perio-
dized training: an elite training group case study. Int J Sports 
Physiol Perform. 2018;13(6):755–62.
 145. Salo AI, Bezodis IN, Batterham AM, Kerwin DG. Elite sprint-
ing: are athletes individually step-frequency or step-length reli-
ant? Med Sci Sports Exerc. 2011;43(6):1055–62.
 146. Hicks D, Schuster J, Samozino P, Morin J-B. Improving 
mechanical effectiveness during sprint acceleration: prac-
tical recommendations and guidelines. Strength Cond J. 
2019;42(2):45–62.
 147. Moir G. Strength and conditioning. A biomechanical approach. 
Burlington: Jones & Bartlett Publishers; 2015.
 148. Eston R, Reilly T. Kinanthropometry and exercise physiology 
laboratory manual: tests, procedures and data: volume two: 
physiology. Routledge; 2013. p. 362.
 149. Douglas J, Pearson S, Ross A, McGuigan M. Reactive and eccen-
tric strength contribute to stiffness regulation during maximum 
velocity sprinting in team sport athletes and highly trained sprint-
ers. J Sport Sci. 2019:1–9.
 150. Morin J-B, Samozino P. Interpreting power-force-velocity pro-
files for individualized and specific training. Int J Sports Physiol 
Perform. 2016;11(2):267–72.
 151. Mendez-Villanueva A, Buchheit M, Kuitunen S, Douglas A, Pel-
tola ESA, Bourdon P. Age-related differences in acceleration, 
maximum running speed, and repeated-sprint performance in 
young soccer players. J Sport Sci. 2011;29(5):477–84.
 152. Vescovi JD, Mcguigan MR. Relationships between sprint-
ing, agility, and jump ability in female athletes. J Sport Sci. 
2008;26(1):97–107.
 153. Little T, Williams AG. Specificity of acceleration, maximum 
speed, and agility in professional soccer players. J Strength Cond 
Res. 2005;19(1):76–8.
 154. Colyer SL, Nagahara R, Salo AI. Kinetic demands of sprinting 
shift across the acceleration phase: novel analysis of entire force 
waveforms. Scand J Med Sci Sports. 2018;28(7):1784–92.
 155. Wild J, Bezodis NE, Blagrove R, Bezodis I. A biomechanical 
comparison of accelerative and maximum velocity sprinting: 
Specific strength training considerations. Prof Strength Cond. 
2011;21:23–37.
 156. Slawinski J, Termoz N, Rabita G, Guilhem G, Dorel S, Morin 
JB, et al. How 100-m event analyses improve our understanding 
of world-class men’s and women’s sprint performance. Scand J 
Med Sci Sports. 2017;27(1):45–54.
 157. Schache AG, Brown NA, Pandy MG. Modulation of work and 
power by the human lower-limb joints with increasing steady-
state locomotion speed. J Exp Biol. 2015;218(15):2472–81.
 158. Whitehead S, Till K, Weaving D, Jones B. The use of microtech-
nology to quantify the peak match demands of the football codes: 
a systematic review. Sport Med. 2018;48(11):2549–75.
 159. Taylor JB, Wright AA, Dischiavi SL, Townsend MA, Marmon 
AR. Activity demands during multi-directional team sports: a 
systematic review. Sport Med. 2017;47(12):2533–51.
 160. Phibbs PJ, Jones B, Read DB, Roe GA, Darrall-Jones J, Weakley 
JJ, et al. The appropriateness of training exposures for match-
play preparation in adolescent schoolboy and academy rugby 
union players. J Sport Sci. 2018;36(6):704–9.
 161. Phibbs PJ, Jones B, Roe GA, Read DB, Darrall-Jones J, Weakley 
JJ, et al. We know they train, but what do they do? Implications 
for coaches working with adolescent rugby union players. Int J 
Sports Sci Coach. 2017;12(2):175–82.
 162. Wellman AD, Coad SC, Goulet GC, McLellan CP. Quantification 
of competitive game demands of ncaa division i college football 
players using global positioning systems. J Strength Cond Res. 
2016;30(1):11–9.
 163. Hartwig TB, Naughton G, Searl J. Motion analyses of adoles-
cent rugby union players: a comparison of training and game 
demands. J Strength Cond Res. 2011;25(4):966–72.
 164. Jiménez-Reyes P, Cross M, Ross A, Samozino P, Brughelli M, 
Gill N, et al. Changes in mechanical properties of sprinting dur-
ing repeated sprint in elite rugby sevens athletes. Eur J Sports 
Sci. 2019;19(5):585–94.
 165. Nagahara R, Morin J-B, Koido M. Impairment of sprint mechani-
cal properties in an actual soccer match: a pilot study. Int J Sports 
Physiol Perform. 2016;11(7):893–8.
 166. de Souza E. Molecular adaptations to concurrent strength and 
endurance training. In: Schumann M, Rønnestad BR, editors. 
Concurrent aerobic and strength training: scientific basics and 
285
Training Medium to Long Sprint Performance in Football Athletes
practical applications. Cham: Springer International Publishing; 
2019. p. 99–123.
 167. Hunter JP, Marshall RN, McNair PJ. Relationships between 
ground reaction force impulse and kinematics of sprint-running 
acceleration. J Appl Biomech. 2005;21(1):31–43.
 168. Lockie RG, Murphy AJ, Scott BR, Janse de Jonge XA. Quanti-
fying session ratings of perceived exertion for field-based speed 
training methods in team sport athletes. J Strength Cond Res. 
2012;26(10):2721–8.
 169. Lockie RG, Murphy AJ, Callaghan SJ, Jeffriess MD. Effects of 
sprint and plyometrics training on field sport acceleration tech-
nique. J Strength Cond Res. 2014;28(7):1790–801.
 170. Lockie RG, Murphy AJ, Schultz AB, Knight TJ, de Jonge XAJ. 
The effects of different speed training protocols on sprint accel-
eration kinematics and muscle strength and power in field sport 
athletes. J Strength Cond Res. 2012;26(6):1539–50.
 171. Harrison AJ, Bourke G. The effect of resisted sprint training on 
speed and strength performance in male rugby players. J Strength 
Cond Res. 2009;23(1):275–83.
 172. Selye H. Stress and the general adaptation syndrome. BMJ. 
1950;1(4667):1383.
 173. Billaut F, Bishop D. Muscle fatigue in males and females during 
multiple-sprint exercise. Sport Med. 2009;39(4):257–78.
 174. Perrey S, Racinais S, Saimouaa K, Girard O. Neural and muscu-
lar adjustments following repeated running sprints. Eur J Appl 
Physiol. 2010;109(6):1027–36.
 175. Jimenez-Reyes P, Pareja-Blanco F, Cuadrado-Peñafiel V, Mor-
cillo J, Párraga J, González-Badillo J. Mechanical, metabolic 
and perceptual response during sprint training. Int J Sport Med. 
2016;37(10):807–12.
 176. Cissik JM. Means and methods of speed training. Part I Strength 
Cond J. 2004;26(4):24–9.
 177. Schmidt RA, Wrisberg CA. Motor learning and performance: a 
situation-based learning approach. Human kinetics; 2008.
 178. Kivi DM. A kinematic comparison of the running A and B drills 
with sprinting [Master of science]. National Library of Canada: 
The University of Manitoba; 1997.
 179. West T, Robson S. Running drills-are we reaping the benefits. 
5th ed. Mountain View: TAFNEWS Press; 2000. p. 64–7.
 180. Jakalski K. Contemporary research and sprinting: Reconsider-
ing the conceptual paradigm of running mechanics. Track Field 
Coach Rev. 2002;75(1):21–2.
 181. Cahill M, Cronin JB, Oliver JL, Clark K, Cross MR, Lloyd RS. 
Sled pushing and pulling to enhance speed capability. Strength 
Cond J. 2019;41(4):94–104.
 182. Kawamori N, Newton RU, Hori N, Nosaka K. Effects of weighted 
sled towing with heavy versus light load on sprint acceleration 
ability. J Strength Cond Res. 2014;28(10):2738–45.
 183. Zafeiridis A, Saraslanidis P, Manou V, Ioakimidis P. The effects 
of resisted sled-pulling sprint training on acceleration and maxi-
mum speed performance. J Sports Med Phys Fit. 2005;45(3):284.
 184. Makaruk B, Sozański H, Makaruk H, Sacewicz T. The effects of 
resisted sprint training on speed performance in women. Hum 
Mov. 2013;14(2):116–22.
 185. Cronin J, Hansen K, Kawamori N, Mcnair P. Effects of weighted 
vests and sled towing on sprint kinematics. Sports Biomech. 
2008;7(2):160–72.
 186. Alcaraz PE, Palao JM, Elvira JL, Linthorne NP. Effects of 
three types of resisted sprint training devices on the kinemat-
ics of sprinting at maximum velocity. J Strength Cond Res. 
2008;22(3):890–7.
 187. Kawamori N, Newton R, Nosaka K. Effects of weighted sled 
towing on ground reaction force during the acceleration phase 
of sprint running. J Sport Sci. 2014;32(12):1139–45.
 188. Cross MR, Brughelli ME, Cronin JB. Effects of vest load-
ing on sprint kinetics and kinematics. J Strength Cond Res. 
2014;28(7):1867–74.
 189. Jiménez-Reyes P, Samozino P, Brughelli M, Morin J-B. Effec-
tiveness of an individualized training based on force-velocity 
profiling during jumping. Front Physiol. 2017;7:677.
 190. Hill A, Long C, Lupton H. The effect of fatigue on the relation 
between work and speed, in contraction of human arm muscles. 
J Physiol. 1924;58(4–5):334–7.
 191. Haugen T, Tønnessen E, Seiler S. 958 and 1049: nearing the citius 
end for 100 m? Int J Sports Physiol Perform. 2015;10(2):269–72.
 192. Bolger R, Lyons M, Harrison AJ, Kenny IC. Coaching sprint-
ing: expert coaches’ perception of resistance-based training. Int 
J Sports Sci Coach. 2016;11(5):746–54.
 193. Healy R, Kenny IC, Harrison AJ. Resistance training practices 
of sprint coaches. J Strength Cond Res. 2019. (Publish Ahead 
of Print)
 194. Whelan N, Kenny IC, Harrison AJ. An insight into track and 
field coaches’ knowledge and use of sprinting drills to improve 
performance. Int J Sports Sci Coach. 2016;11(2):182–90.
 195. Mendiguchia J, Martinez-Ruiz E, Morin JB, Samozino P, 
Edouard P, Alcaraz PE, et al. Effects of hamstring-emphasized 
neuromuscular training on strength and sprinting mechanics in 
football players. Scand J Med Sci Sports. 2015;25(6):e621–9.
 196. Emmonds S, Heyward O, Jones B. The challenge of applying 
and undertaking research in female sport. Sport Med Open. 
2019;5(1):51.
 197. Kraemer WJ, Ratamess NA, Nindl BC. Recovery responses of 
testosterone, growth hormone, and IGF-1 after resistance exer-
cise. J Appl Physiol. 2016;122(3):549–58.
 198. Oliver JL, Lloyd RS, Rumpf MC. Developing speed throughout 
childhood and adolescence: the role of growth, maturation and 
training. Strength Cond J. 2013;35(3):42–8.
 199. Rumpf MC, Cronin JB, Oliver JL, Hughes M. Assessing youth 
sprint ability–Methodological issues, reliability and performance 
data. Pediatr Exerc Sci. 2011;23(4):442–67.
 200. Rodríguez-Rosell D, Franco-MÁrquez F, Mora-Custodio R, 
GonzÁlez-Badillo JJ. Effect of high-speed strength training on 
physical performance in young soccer players of different ages. 
J Strength Cond Res. 2017;31(9):2498–508.
 201. Coutts AJ, Reaburn P, Piva TJ, Rowsell GJ. Monitoring for 
overreaching in rugby league players. Eur J Appl Physiol. 
2007;99(3):313–24.
 202. McMillan K, Helgerud J, Grant S, Newell J, Wilson J, Macdonald 
R, et al. Lactate threshold responses to a season of professional 
British youth soccer. Br J Sports Med. 2005;39(7):432–6.
 203. Markovic G, Mikulic P. Neuro-musculoskeletal and performance 
adaptations to lower-extremity plyometric training. Sport Med. 
2010;40(10):859–95.
 204. Yanci J, Los Arcos A, Camara J, Castillo D, García A, Castagna 
C. Effects of horizontal plyometric training volume on soccer 
players’ performance. Res Sport Med. 2016;24(4):308–19.
 205. Wewege M, Devonshire J, Hansford H, McAuley J, Jones M. 
Comment on: “The training of short distance sprint performance 
in football code athletes: a systematic review and meta-analysis". 
Sport Med. 2021;51:1–5.
 206. Cross MR, Brughelli M, Samozino P, Brown SR, Morin J-B. 
Optimal loading for maximising power during sled-resisted 
sprinting. Int J Sports Physiol Perform. 2017:1–25.
 207. Samozino P, Morin J-B, Hintzy F, Belli A. A simple method for 
measuring force, velocity and power output during squat jump. 
J Biomech. 2008;41(14):2940–5.
 208. Samozino P, Rabita G, Dorel S, Slawinski J, Peyrot N, Saezdeil-
larreal E, et al. A simple method for measuring power, force, 
velocity properties, and mechanical effectiveness in sprint run-
ning. Scand J Med Sci Sport. 2016;26(6):648–58.
286
 
B. Nicholson et al.
Authors and Affiliations
Ben Nicholson1 · Alex Dinsdale1 · Ben Jones1,2,3,4,5 · Kevin Till1,2
 * Ben Nicholson 
 
B.t.nicholson@leedsbeckett.ac.uk
1 
Carnegie Applied Rugby Research (CARR) Centre, 
Carnegie School of Sport, Leeds Beckett University, 
Headingley Campus, Leeds LS6 3QS, UK
2 
Leeds Rhinos Rugby League Club, Leeds, UK
3 
England Performance Unit, The Rugby Football League, 
Leeds, UK
4 
School of Science and Technology, University of New 
England, Armidale, NSW, Australia
5 
Division of Exercise Science and Sports Medicine, 
Department of Human Biology, Faculty of Health Sciences, 
The University of Cape Town and the Sports Science 
Institute of South Africa, Cape Town, South Africa

The Training of Medium- to Long-Distance Sprint Performance in Football Code Athletes: A Systematic Review and Meta-analysis.
Nicholson, Ben,Dinsdale, Alex,Jones, Ben,Till, Kevin