*4.1. Spectral Characteristics of Kelp as It Is Submerged*

A broad R0+ peak across the NIR region was observed for surface measurements of *Nereocystis* as a result of the interaction of light with the cellular structure of the kelp [51]. Once submerged, our experiment showed two key changes in the NIR region of the kelp spectra (Figure 3), both due to characteristic absorption features of water: (1) the splitting of the single broad NIR peak into two narrower RE and NIR peaks due to prominent water absorption feature at 760 nm [52] and (2) higher R0+ at the RE peak versus the NIR peak, resulting from the continually increasing absorption of light by water above 600 nm [22]. Since *Macrocystis* and *Nereocystis* are spectrally similar to one another in the NIR region [15,18,27,50], and the changes seen in the spectra of submerged kelp are due to properties of water absorption, we expect that the spectral results of this experiment are generally applicable to both *Macrocystis* and *Nereocystis* canopies, making these findings relevant for surface-canopy forming kelp species globally.

The results of these experiments were generally in line with our expectations according to similar studies of submerged aquatic vegetation [24,25], although there were some interesting phenomena seen in the spectra that are worth noting. In the visible region of the spectra, R0+ in the red wavelength range decreased with depth, as expected. However, the R0+ at the absorption feature between 400 and 550 nm increased slightly with submersion. We hypothesize that this increase in R0+ is due to the scattering of light by the conditions of the water optical constituents, thus increasing the R0+ with depth. As such, we suspect that this increase in R0+ may be specific to the water conditions during the experiment and may not have occurred if the water had contained more optical constituents that absorb blue light, such as colored dissolved organic matter. Another interesting phenomenon noted in the floating kelp spectra was what appeared to be a sunlight-induced chlorophyll fluorescence (SICF) peak at 761 nm (Figure 4). Within the NIR region, photosynthetic organisms generally have a broad SICF peak centered at 740 nm [53]. However, due to the high magnitude of the NIR reflectance, the SICF is usually only visible as a small, narrow peak centered at 761 nm. Typically, the R0+ within the NIR wavelength range overwhelms the signal from SICF, however, atmospheric gasses highly absorb incoming irradiance at 761 nm, which can create a fill-in effect by the SICF in this region [53]. While this phenomenon has been correlated with photosynthetic output and general health of terrestrial vegetation and phytoplankton [53,54], we are not aware of any publications that report an SICF peak in kelp spectra, and this may present an opportunity for future hyperspectral research. Once kelp was submerged the SICF feature was dampened, and therefore future research should take note of the amount of kelp at the surface if attempting to derive information from an SICF peak.

#### *4.2. NIR Differences between Nereocystis Bulbs and Blades*

The magnitude of reflectance across the NIR region in vegetation is generally due to the cellular structure of the respective tissues [55]. Both *Nereocystis* bulb and blade tissues are composed of the same three cellular layers: the meristoderm, the medulla, and the cortex. The meristoderm is a thin chloroplast-packed epidermal layer that surrounds the entire

individual [56], and the medulla is a complex web of filaments that acts as a transportation system within the kelp, composing the innermost layer of kelp tissue [57,58]. Between these two layers is the cortex, which connects the meristoderm to the medulla, and generally provides structural support for the kelp [56,59]. Given this structural arrangement, we speculate that the NIR signal from bulbs is consistently higher compared to the blades' signal because (1) the bulb cortex is many times thicker than the blade cortex [57,59]; and (2) the gas cavity of the bulb is lined by the medulla [57], creating a high surface area with many large refractive differences—similar to the mesophyll layer of a terrestrial leaf [55,60]. In comparison, the blade medulla is housed in a gelatinous extracellular matrix between cells [57], and with no gas cavities, the refractive differences are much smaller, allowing for increased transmittance of NIR light through the blades [55]. For our experiment, spectral measurements for blades were taken using a single blade wrapped around the polyethylene frame with only slight overlap between the edges of the blade. However, *Nereocystis* individuals may have between 30–60 blades each. Overall, a thicker mass of blade tissues due to high overlap may result in higher R0+ in the RE and NIR wavelength ranges than seen in this experiment.


*3.3. Indication of the IES CMD*

The IES CMD was used in our clinic for different indications. The possible applications included the penetration into a cochlea obstructed with fibrotic or ossified tissue, which could not be achieved by common electrodes due to their lack of stiffness and their inability to dilate the lumen. It was also used as an aid in re-implantations. Furthermore, the IES CMD was utilised as an instrument to estimate the depth of possible electrode insertion in the malformed cochleae.

Thus, the IES was deployed in the evaluated IES group to measure the depth of the cochlea prior to implantation (*n* = 7; 21.2%), due to intracochlear tissue which hampered the insertion of the electrode array (*n* = 10; 30.3%), as well as to ossify the basal turn or the round window region (*n* = 9; 27.3%). The IES was also used four times (12.1%) as a tool during device re-implantation to clear out fibrotic tissue. In three cases (9.1%), the reason for using the IES was to eliminate any resistance that occurred when trying to insert the stimulation electrode.

In the evaluation above, a variable was disregarded, depending on whether the surgical intervention was a first implantation or a re-implantation of a CI. In the IES group, 22 subjects (66.7%) underwent first implantation with an average duration of hearing loss of 8.57 years (range: 1 month–31 years). Eleven cases (33.3%) underwent re-implantation surgery due to soft failure or malfunction of the implant. The previous implant was in situ for a mean of 9.82 years (range: 6 month–31 years).

In the REGULAR group, patients were matched to the IES group (*n* = 22 first implantations and *n* = 11 re-implantations). An exact adjustment to the duration of hearing loss or the duration of the previous implant in situ was not possible due to the limited number of patients available.

In the SPLIT group, six cases (75%) were provided with a Split-Array CMD during the initial implantation procedure and showed an average duration of deafness of 5.04 years (range: 6 month–24 years). Two cases (25%) received a Split-Array CMD during a reimplantation procedure after 25 years in the first case and after 1 year in the second case.


**Implementation of Distributed Control Architecture for Multiple Robot Systems Using Petri Nets**

Gen'ichi Yasuda

74 Petri Nets – Manufacturing and Computer Science

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[25] INA. (Integrated Net Analyzer), A Software Tool for Analysis of Petri Nets. Version 2.2, 31.07. 2003. [Online]. Available: http://www.informatik.hu-berlin.de/~starke/ina.html. [26] Huang YS, Pan YL. Enhancement of An Efficient Liveness-Enforcing Supervisor for Flexible Manufacture Systems. Int J Adv Manuf Technol. 2010;48:725-737. (DOI:

[27] Huang YS, Pan YL. An Improved Maximally Permissive Deadlock Prevention Policy Based on the Theory of Regions and Reduction Approach. IET Control Theory Appl.

[28] Pan YL, Huang YS. Solutions for Deadlocked Problem of FMSs Using Theory of Regions. Adv Mat Res. 2011 ;314-346 :535-538. (DOI:10.4028/www.scientific.net/AMR.314-316.535.) [29] Huang YS, Pan YL, Zhou MC. Computationally Improved Optimal Deadlock Control Policy for Flexible Manufacturing Systems. IEEE Trans Syst Man Cybern A Syst

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/50577


**3. Results**

The visual data that are generally considered as resistance data did not show any decrease in the 16-day inoculation period (Table 3). At the 8th and 16th day, larger values were found, but values found on the other four days were at the same level. This means that the visual scores did not show any sign of the susceptibility window in the first 16 days, i.e., the whole period acted as a long susceptibility window without signs of decrease, even following the flowering; a decrease was anticipated. The resistance differences between genotypes were highly significant; F569/Kö was the best at 5.18% and GK Futár was the most susceptible at 18.9% disease index. The cultivars reacted rather inconsistently, the most resistant varieties gave the same numbers across the whole period, but others showed variable performance, as shown by the diverging correlation coefficients (Table 3A). On the other hand, the correlations between variety reactions and different inoculation dates were much higher, and only several were not significant at *p* = 0.05. This would indicate a reasonable stability of responses across the different inoculation dates.

**Table 3.** Susceptibility window for *Fusarium* head blight (FHB) in wheat, disease index data, as the % of infected spikelets, across four independent isolates, 2013–2014, Szeged.

\*\*\* *p* = 0.001, \*\* *p* = 0.01, \* *p* = 0.05.

The FDK data (Table 4A) showed a different picture. Between mid-flowering and eight days after inoculation, except the fourth day (it is significantly lower than the initial and eighth day values), stability was mostly seen, thereafter a sharp, more than 50% reduction was recorded, followed by an additional one-third decrease that remained stable afterwards also by the 16th day. This decrease is highly significant, with limit of significant difference (LSD) 5% at 1.76%. The cultivar differences between means were five-fold, the most resistant showed 5.43% FDK and the most susceptible showed 25.1%. The variation width was near 20%, the LSD 5% value was 1.91, so a roughly ten-fold difference existed between this and the variation width. This meant a rather stable reaction in the first eight days, experimental proof to the presence of the susceptibility window. We should say that the correlations between genotype reactions (Table 4B) were much closer than they were in the FHB disease index, all correlations were significant between *p* = 0.05 and *p* = 0.001. This indicated a similar response of the cultivar reactions in the 16 day inoculation period. The reduction can be seen at all resistance levels. The rate was similar, but the data were much lower in the more resistant genotypes, which explains the closer correlation values. The correlations between the response of the genotypes to different inoculation data were also similar (Table 4C), but in several cases the level was lower, and three correlations were not significant.

**Table 4.** Susceptibility window for FHB in wheat, FDK data as percentage across fours independent isolates, 2013–2014, Szeged.

\*\*\* *p* = 0.001, \*\* *p* = 0.01, \* *p* = 0.05.

The DON data (Table 5A) for the 0–8th day showed a stable mean DON contamination between 9.23 and 9.81 mg/kg concentration. Most of the cultivars were stable, and the differences between inoculation dates were not large. The decrease of DON contamination was very sharp after the eighth day, only one-third remained from the eighth day amount. This decreased by an additional 50% to the 13th day and a further significant decrease was recorded between the 11th and 13th day. The additional small decrease up to the 16th day was not significant.

**Table 5.** Susceptibility window for FHB in wheat, DON contamination, mg/kg across four independent isolates, 2013–2014, Szeged.

\*\*\*\* *p* = 0.0001, \*\*\* *p* = 0.001, \*\* *p* = 0.01, \* *p* = 0.05.

Therefore, counting by FDK and FDK, the susceptibility window lasted for the same eight days for all resistance classes. Also, different cultivar reactions can be recognized. The most resistant F569/K˝o had the lowest DON at about 3 mg/kg, and this could decrease the DON level below the EU limit of 1.25 mg/kg. The case of the GK Fény variety is important, since in 2010 the large FHB epidemic damaged it only moderately in contrast to GK Garaboly, which had the highest DON contamination at flowering. We observed a similar phenomenon with GK Csillag; its resistance was better than GK Fény. Attention should be paid to the fact that in normal testing only the first inoculation was made and the others were not existing. The data show also that to see the resistance behavior for delicate and highly important cultivars, the 11th- and 16th-day inoculation is suggested. By this way we could detect cultivars like GK Csillag, that was rather susceptible at mid-anthesis and could be discarded based on disease index, but in later inoculation dates it gave the second place following the most resistant genotype. Correlations between genotype reactions were very close, as it was for FDK, but at a higher level (Table 5B). Therefore, cultivars across inoculation times behaved similarly. We should also consider that good correlations existed for the DON data only between the first four inoculation dates, during the later inoculation periods no significant correlations were found (Table 5C). This means that the resistance relationships do not automatically follow this range later on, so therefore unexpected phenomena may occur. Only one example, GK Csillag, had 10 ppm DON content at mid-flowering and 0.64 ppm on the last inoculation. F569/K˝o started with 2.98 and finished with 0.57 mg/kg, practically the same value. The DON data in graphical form (Figure 2) shows the genotype reaction during the six inoculation periods. The DON regulation seemed to be more complicated than usually expected.

**Figure 2.** Susceptibility window against FHB in wheat, DON contamination of the wheat genotypes at different inoculation dates, Szeged, 2013 and 2014. LSD 5% genotypes: 1.12; inoculation days: 1.04.

The general means of the isolate data showed rather large differences for DI, but for all isolates the genotype differences were significant (Figure 3). Fg 19.42 isolate was the most aggressive; the others displayed only 40–50% of its value. The FDK data showed larger isolate differences than DI, the resistance differentiation less expressed for the three less aggressive isolate (Figure 4). We had the largest difference between isolates for DON compared to DI and FDK (Figure 5); the Fg 19.42 isolate kept its excellent performance, the Fc 12375 showed yet significant differences, and the two least aggressive isolates did not show a proper differentiation, as they showed for DI and FDK. It seems that even at medium aggressiveness in DI or FDK can cause a problem in DON response.

**Figure 3.** Susceptibility window in wheat, disease index (DI) % general means for the four isolates in the seven genotypes differing in resistance, 2013 and 2014, Szeged. LSD 5% between any data: 2.01.

**Figure 4.** Susceptibility window in wheat, FDK % for the general means of the four isolates in the seven genotypes differing in resistance, 2013 and 2014, Szeged. LSD 5% between any data: 3.81.

**Figure 5.** Susceptibility window in wheat, DON mg/kg values for the general means of the four isolates in the seven genotypes differing in resistance, 2013 and 2014, Szeged. LSD 5% between any data: 2.25.

Looking at the three traits across cultivars, we observed a clear picture of how the FHB data did not show any sign of decreasing during the 16-day period. However, the FDK and DON showed much similarity (Figure 6). The correlation for disease index/FDK was r = −0.1639, DI/DON was r = −0.2347, and FDK/DON was highly significant at r = 0.9673 with *p* = 0.01. This is again an argument that FDK is a more important trait to characterize resistance level than DI.

**Figure 6.** FHB disease index, FDK and DON means across genotypes at six inoculation time points during 2013 and 2014, Szeged

The general means for the genotypes and traits showed several tendencies (Figure 7). The most resistant F569/K˝o showed the lowest values for all three traits. The F569/81.F.379 had an FDK value twice as large compared to the FHB than the most resistant genotype. As we have the mean values of six inoculations, the resistance data are more reliable than only with one inoculation at full flowering stage. Thus, this two-fold difference does not seem to have occurred due to random chance. It is remarkable that GK Csillag was for visual symptoms among the more susceptible cultivars, but one of the best in counting for DON.

**Figure 7.** Susceptibility window against FHB in wheat showing the general pattern for genotypes and traits FHB (DI) %, FDK %, and DON mg/kg, 2013 and 2014, Szeged

The ANOVA of the three traits (Table 2) showed highly significant main effects. As the four-way interaction was significant in all tests, we also calculated the F-value for the AxBxCxD interaction, but basically the result remained very similar to what we found for F-values against the "Within." Beyond this, for us especially, the two-way interactions were important, which contained the main effect Genotype (A) and the other three main effects: Inoculations, Isolates, and Year (AxB, AxC, AxD). When the main effect is dominant above the two-way interaction, it means that the genotype performance is stable under the different inoculation times. Thus, in different inoculation times, even we found a significant influence, but with a modifying effect only and the probability of a very variable performance at these conditions was very low. When the main effect does not differ significantly from the interaction, it means that the stability of the main e ffect is poor, and we may count it with di ffering responses. It is also important whether the traits responded similarly or di fferently. There were examples for both. The Genotype (A) main e ffect was dominant over the AxB interaction (MS = 24.94); its level is shown by asterisks. This dominance was very strong at FHB, medium for FDK, and lowest for DON, so the traits behaved di fferently, even the level of significance was *p* = 0.001. For the inoculation time (B), this was opposite to AxB; for FHB data this was just above the limit of significance (as between inoculations the di fference was small), but it was very high for FDK and DON, where the last inoculations gave only small DON content compared to the first three inoculation dates. The Genotype (A) main e ffect to AxC interaction was highly significant and the tendency corresponded to the fact we found with the Genotype main e ffect tested against the AxB interaction. The Isolate (C) effect was similarly dominant against AxC interaction for all traits, indicating that the main factor was the Isolate e ffect, being rather stable at di fferent traits. Regarding the Inoculation date (B) interaction against the BxC interaction, we did not see significant di fferences except FDK; for FHB and DON the result was neutral. However, the Isolate (C) e ffect was highly significant for all traits against BxC interaction, but the smallest number was found for DON, and even it was highly significant. The Genotype e ffect against AxD was significant at *p* = 0.05 for FHB and FDK, but no di fference with DON, indicating no special interaction between Genotype and Year. The Year e ffect was highly significant for all traits against AxD, showing a strong dominance of the year over the interaction, i.e., the stability of the Genotype performance in di fferent years.

The degrees of freedom for the interactions were the same as those listed in the main ANOVA (Table 2). We counted the means for two years for all cultivars and isolates separately for each trait and then calculated the Pearson correlation coe fficients for all isolates between traits (Table 6). All correlations were significant for isolate 3. None were significant for isolate 4. Two were significant for isolate 1 and only one for isolate 2. Furthermore, all were highly significant for the mean reactions. One general conclusion is certain, namely, the di fferent isolates do not respond the same way. Therefore, the use of a single isolate can be risky. Here we should remark that any of the four isolates can be one in a paper where only one inoculum was used. For a publication where only one was available for phenotyping, a large variation might occur, and the genetic meaning of the data is instable. This is valid for mapping work, but also for resistance evaluation of the genotypes in breeding or cultivar registration.

**Table 6.** Susceptibility window against FHB in wheat, 2013 and 2014. Correlation between di fferent traits and the corresponding trait values. For orientation, the general means of the data are given.

\*\*\* *p* = 0.001, \*\* *p* = 0.01, \* *p* = 0.05; DI = disease index; F.c.: *F. culmorum*; F.g.: *F. graminearum.*


*Article* **A Multidisciplinary Investigation into the Talent Development Processes at an English Football Academy: A Machine Learning Approach**

**Adam L. Kelly 1,\* , Craig A. Williams 2 , Rob Cook 1 , Sergio Lorenzo Jiménez Sáiz 3 and Mark R. Wilson 2**

**Abstract:** The talent development processes in youth football are both complex and multidimensional. The purpose of this two-fold study was to apply a multidisciplinary, machine learning approach to examine: (a) the developmental characteristics of under-9 to under-16 academy players (*n* = 98; Study 1), and (b) the characteristics of selected and deselected under-18 academy players (*n* = 18; Study 2). A combined total of 53 factors cumulated from eight data collection methods across two seasons were analysed. A cross-validated Lasso regression was implemented, using the glmnet package in R, to analyse the factors that contributed to: (a) player review ratings (Study 1), and (b) achieving a professional contract (Study 2). Results showed non-zero coefficients for improvement in subjective performance in 15 out of the 53 analysed features, with key findings revealing advanced percentage of predicted adult height (0.196), greater lob pass (0.160) and average dribble completion percentage (0.124), more total match-play hours (0.145), and an older relative age (BQ1 vs. BQ2: −0.133; BQ1 vs. BQ4: −0.060) were the most important features that contributed towards player review ratings. Moreover, PCDEQ Factor 3 and an ability to organise and engage in quality practice (PCDEQ Factor 4) were important contributing factors towards achieving a professional contract. Overall, it appears the key factors associated with positive developmental outcomes are not always technical and tactical in nature, where coaches often have their expertise. Indeed, the relative importance of these factors is likely to change over time, and with age, although psychological attributes appear to be key to reaching potential across the academy journey. The methodological techniques used here also serve as an impetus for researchers to adopt a machine learning approach when analysing multidimensional databases.

**Keywords:** talent identification; expertise; psychological characteristics; physical characteristics; technical and tactical; elite youth soccer

#### **1. Introduction**

It is widely acknowledged that the talent development processes in youth football are both complex and multidimensional [1]. Although various factors have been identified to influence the talent development processes in youth football, only a few multidisciplinary studies exist. As an example, Huijgen and colleagues [2] applied a battery of objective field tests and questionnaires within the four domains of technical, tactical, physiological, and psychological characteristics to players aged 16 to 18 years. It was revealed that selected players outperformed their deselected counterparts, whereby performance in the technical skill of dribbling, the tactical characteristics of positioning and deciding, and the physiological attribute of sprinting correctly classified 69% of talented players. Moreover, Forsman and colleagues [3] examined multiple factors of youth football players

**Citation:** Kelly, A.L.; Williams, C.A.; Cook, R.; Sáiz, S.L.J.; Wilson, M.R. A Multidisciplinary Investigation into the Talent Development Processes at an English Football Academy: A Machine Learning Approach. *Sports* **2022**, *10*, 159. https://doi.org/ 10.3390/sports10100159

Academic Editors: Adam Baxter-Jones and Michael Chia

Received: 19 July 2022 Accepted: 13 October 2022 Published: 19 October 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

at aged 15 years that eventually contributed to successful football performance at aged 19 years. Performance at aged 19 years was associated with technical (i.e., passing), tactical (i.e., centering), physiological (i.e., agility), and psychological (i.e., motivation) attributes that were displayed at aged 15 years. In addition, Zuber and colleagues [4] observed holistic patterns as an instrument for predicting the performance in promising young football players over a three-year period. They revealed that highly skilled players scored above average on all technical, physiological, and psychological factors. Collectively, this research reinforces the importance of providing a multidimensional research methodology in youth football when exploring the talent development processes.

The multidisciplinary nature of the talent development process is also reflected in several theoretical (e.g., Personal Assets Framework [5,6]) and practical (e.g., Locking Wheel Nut Model [7]) frameworks. One practically based model that is particularly relevant to the talent development processes in youth football is The Football Association's Four Corner Model (FCM) [8]. The FCM is often adopted in professional football clubs and organisations in England (amongst other countries), which advocates the assessment and development of players according to: (a) *technical/tactical*, (b) *physical*, (c) *psychological*, and (d) *social* attributes. Towlson and colleagues [9] applied the FCM to their qualitative methodology whilst examining the perceived importance that practitioners placed on the four sub-components during player selection in academy football. It was discovered that the psychological sub-component was rated significantly higher than the other three sub-components. Likewise, Kelly and colleagues [10] adopted the FCM in a quantitative analysis of factors differentiating those who 'play-up' an age-group compared to those who do not based on age phase (i.e., Foundation Development Phase [FDP]: under-9 to under-11; Youth Development Phase [YDP]: under-12 to under-16). Technical/tactical and social characteristics appeared to differentiate those who play-up compared to those who do not at ages 8 to 11 years, whereas there were measures representing all four sub-components from the FCM for those aged 11 to 16 years. Since the FCM is a tool that is perceived to be relevant and useful for football coaches and practitioners [11,12], it provides a salient framework for understanding the factors associated with talent development and thus may facilitate important knowledge translation.

With talent development being inherently multifactorial, explorative studies must employ analysis techniques that can handle multiple competing, and possible correlated, features. Traditional regression techniques inherently struggle to estimate model coefficients when the number of independent variables (IVs) is comparable to the number of observations, though the emerging family of feature selection algorithms from the machine learning discipline offer possible solutions. In the case of regression, feature selection is often achieved by including a penalization term during the model fit, such as the Lasso first proposed by Tibshirani [13], creating a scenario whereby the optimal model is that which explains the most of the data with the fewest parameters. These penalized regression routines present themselves as a potential tool for rapidly summarising observational retrospective data, as well as generating new insights and testable hypotheses. The advantage of such approaches is that they can effectively process large amounts of data for key features in a cost effective and timely manner. Within the remit of talent development, the machine learning approaches here do not aim to answer the deep questions of what leads to optimal performance, but instead seek to demonstrate a method to leverage some of the quantities of available data to generate new hypotheses and insights [14].

Although the use of machine learning as a statistical analysis method in sport science research is very much in its infancy, there is an increasing amount of literature that has applied such methods, including competition outcome predictions [15], human movement [16], practice history [17], and injury risk [18]. From a talent development perspective, preliminary studies in cricket have used non-linear machine learning (pattern recognition) techniques to examine various factors that contribute to 'super-elite' status [19,20]. As an example, Jones and colleagues [19] showed how a subset of 18 features (from 658 collected) differentiated 'super-elite' (i.e., high-profile international) and 'elite' (domestic professional) senior batsmen with excellent classification accuracy (96%). Moreover, Musa and colleagues [21] classified and predicted 'high-potential' archers from a set of variables trained on a variation of k-NN algorithms and logistic regression. Weighted k-NN outperformed all the tested models with reasonably good accuracy (83%) for the prediction of 'highpotential' (i.e., top of group) and 'low-potential' (i.e., bottom of group) developmental level (aged 13 to 20 years) archers. Most recently, Owen and colleagues [22] used a Bayesian machine learning approach to identify the physiological and psychosocial models that predict selection to a regional age-grade rugby union team. They showed their physiological models correctly classified 67.55% of all players, whereas their psychosocial models correctly classified 62.26% of all players.

It is also important to consider the intra- and inter-contextual factors when designing and evaluating talent development processes, since predictive features (i.e., technical/tactical, physical, psychological, social) in youth football can differ compared to other sports (e.g., cricket, archery, rugby union) and within football-specific environments (e.g., soccer, beach soccer, sepak takraw [23]), respectively. Thus, this current study aims to add to the growing body of literature that has applied machine learning techniques in sport to better understand talent development processes in youth football. The purpose of this two-fold study was to use machine learning algorithms to: (a) explore the multidimensional developmental characteristics of under-9 to under-16 football academy players based on coaches review ratings, and (b) examine the multidimensional characteristics that differentiated selected and deselected under-18 football academy players based on achieving a professional contract.

#### **2. Exploring the Developmental Characteristics of under-9 to under-16 Football Academy Players**

Professional football academies and governing bodies aim to foster player development pathways towards expertise through adopting evidence-based philosophies (see the Elite Player Performance Plan via The Premier League [24]). In England, young players join an academy on schoolboy terms between the ages of 8 and 16 years (i.e., part-time attendance). At aged 16 years, those players who show continued progress are selected to undertake a two year, full-time youth training scheme known as an academy scholarship. Upon completion of their scholarship, players either sign a professional contract or are released. These developmental stages have been divided into three phases to capture the possible age-specific requirements: (a) FDP (under-9 to under-11), (b) YDP (under-12 to under-16), and (c) Professional Development Phase (PDP; under-17 to under-21). In the pursuit of developing male players towards their respective senior team, professional clubs and organisations continue to invest a significant monetary outlay towards human (e.g., coaches, specialist support staff) and physical (e.g., facilities, specialist equipment) resources [25]. In order to better understand the talent development processes in youth football, it is important to identify factors that may influence the progression of schoolboy players (i.e., FDP and YDP). By doing so, it will also help inform key stakeholders (e.g., coaches, practitioners, policy makers) to create evidence-based policies that will offer each individual the most suitable opportunity to achieve a scholarship and professional contract (i.e., PDP).

The purpose of this study was to examine a range of factors based on the FCM (i.e., technical/tactical, physical, psychological, social) that may have contributed to under-9 to under-16 academy football players' review ratings across two seasons using a machine learning approach.

#### *2.1. Methods*

#### 2.1.1. Sample

Ninety-eight male participants were recruited from under-9 to under-16 age groups. All the participants were from the same tier four English professional football club and their category three academy. The average weekly training and match-play time was

9–10.5 training hours/week with one match-play hour/week for the FDP players, and 10–14.5 training hours/week with one match-play hour/week for the YDP players. Goalkeepers were not included in this study due to their contrasting position-specific requirements [26]. Parental consent and player assent were collected prior to the study commencing. The study was approved by the Ethics Committee of Sport and Health Sciences at the University of Exeter. 10.5 training hours/week with one match-play hour/week for the FDP players, and 10– 14.5 training hours/week with one match-play hour/week for the YDP players. Goalkeepers were not included in this study due to their contrasting position-specific requirements [26]. Parental consent and player assent were collected prior to the study commencing. The study was approved by the Ethics Committee of Sport and Health Sciences at the University of Exeter.

Ninety-eight male participants were recruited from under-9 to under-16 age groups. All the participants were from the same tier four English professional football club and their category three academy. The average weekly training and match-play time was 9–

*Sports* **2022**, *10*, x FOR PEER REVIEW 4 of 17

#### 2.1.2. Measures and Procedures 2.1.2. Measures and Procedures

*2.1. Methods*  2.1.1. Sample

The dataset comprised of eight data collection methods that were collected twice during two football seasons (2014–2015 and 2015–2016) to test year-on-year developmental outcomes (see Figure 1 for a timeline of the data collection). These measures were then allocated into the four sub-components in-line with the FCM: (1) *Technical/Tactical*; (a) technical tests [27,28], (b) match analysis statistics [28], and (c) perceptual-cognitive expertise (PCE) video simulation tests [29]. (2) *Physical*; (a) anthropometric measures, and (b) fitness tests [29]. (3) *Psychological*; (a) the Psychological Characteristics for Developing Excellence Questionnaire (PCDEQ) [30–32]. And, (4) *Social*; (a) Participation History Questionnaire (PHQ) [33], and (b) postcode data [30]. The dataset comprised of eight data collection methods that were collected twice during two football seasons (2014–2015 and 2015–2016) to test year-on-year developmental outcomes (see Figure 1 for a timeline of the data collection). These measures were then allocated into the four sub-components in-line with the FCM: (1) *Technical/Tactical*; (a) technical tests [27,28], (b) match analysis statistics [28], and (c) perceptual-cognitive expertise (PCE) video simulation tests [29]. (2) *Physical*; (a) anthropometric measures, and (b) fitness tests [29]. (3) *Psychological*; (a) the Psychological Characteristics for Developing Excellence Questionnaire (PCDEQ) [30–32]. And, (4) *Social*; (a) Participation History Questionnaire (PHQ) [33], and (b) postcode data [30].

**Figure 1.** Timeline of the data collection. **Figure 1.** Timeline of the data collection.

A combined total of 53 characteristics were cumulated from the eight methods [34]: (1) *Four football-specific technical tests*; (a) ball juggling, (b) slalom dribble, (c) shooting accuracy, and (d) lob pass. (2) *Eight match analysis statistics from across the entire season*; (a) reliability in possession, (b) pass completion, (c) tackle completion, (d) block completion, (e) loose balls retrieved, (f) dribble completion, (g) total touches, and (h) goals scored. (3) *Three PCE video simulation tests*; (a) 'pre' execution occlusion, (b) 'during' execution occlusion, and (c) 'post' execution occlusion. (4) *Eight anthropometric measures*; (a) height, (b) body mass, (c) body mass index, (d) body fat percentage, (e) estimated adult height, (f) percentage of estimated adult height attained, (g) maturity status, and (h) birth quartile.  (6) *Six factors from the 59 item PCDEQ*; (a) Factor 1 (support for long-term success), (b) Factor 2 (imagery use during practice and competition), (c) Factor 3 (coping with performance and developmental pressures), (d) Factor 4 (ability to organise and engage in quality), (e) Factor 5 (evaluating performances and working on weaknesses), and (f) Factor 6 (support from others to compete to my potential). (7) *Ten items from the PHQ*; (a) age started playing football, (b) age started playing academy football, (c) total match-play hours, (d) total coach-led practice hours, (e) total peer-led play hours, (f) total individual practice hours, (g) total football A combined total of 53 characteristics were cumulated from the eight methods [34]: (1) *Four football-specific technical tests*; (a) ball juggling, (b) slalom dribble, (c) shooting accuracy, and (d) lob pass. (2) *Eight match analysis statistics from across the entire season*; (a) reliability in possession, (b) pass completion, (c) tackle completion, (d) block completion, (e) loose balls retrieved, (f) dribble completion, (g) total touches, and (h) goals scored. (3) *Three PCE video simulation tests*; (a) 'pre' execution occlusion, (b) 'during' execution occlusion, and (c) 'post' execution occlusion. (4) *Eight anthropometric measures*; (a) height, (b) body mass, (c) body mass index, (d) body fat percentage, (e) estimated adult height, (f) percentage of estimated adult height attained, (g) maturity status, and (h) birth quartile.  (6) *Six factors from the 59-item PCDEQ*; (a) Factor 1 (support for long-term success), (b) Factor 2 (imagery use during practice and competition), (c) Factor 3 (coping with performance and developmental pressures), (d) Factor 4 (ability to organise and engage in quality), (e) Factor 5 (evaluating performances and working on weaknesses), and (f) Factor 6 (support from others to compete to my potential). (7) *Ten items from the PHQ*; (a) age started playing football, (b) age started playing academy football, (c) total match-play hours, (d) total coach-led practice hours, (e) total peer-led play hours, (f) total individual practice hours, (g) total football hours, (h) total sports played, (i) total multisport hours, and (j) total football and multisport hours. Finally, (8) *six measures from postcode data*; (a) home area code, (b) home financial risk, (c) home social classification, (d) school area code, (e) school financial risk, and (f) school social classification. The procedures for each of these methods have been outlined in our previously published work [27–34], which have been added below (Section 2.1.2) for the convenience of the reader.

Four Football-Specific Technical Tests

Four football-specific technical tests were used to measure technical ability: (a) ball juggling, (b) slalom dribble, (c) shooting accuracy, and (d) lob pass. First, the slalom dribble test required the player to control the ball through nine cones (2 m apart) from the start to the end line and return. The timings were recorded using timing gates (Brower TC Timing System, Draper, Utah, USA), with each player completing two trials and the quicker of the two recorded for analysis. Second, the lob pass test required the player to kick the football from a distance of 20 m into a target area divided into three concentric circles (3 m, 6 m, and 9.15 m in diameter). Each kick was scored by the circle in which the ball initially landed (3, 2, and 1 point, respectively). Ten attempts (five with each foot) were executed with a maximum of 30 points available. Third, the shooting accuracy test required the player to kick the ball at a 16 m wide goal target from a shooting distance of 20 m and central to the goal. The goal was divided into five parallel zones, whereby the centre was, 2 m wide (3 points), with two areas 3 m on each side of the centre (2 points), and two areas 4 m wide at each extreme (1 point). Ten attempts (five with each foot) were executed with a maximum of 30 points available. Fourth, the ball juggling test required the player to keep a football off the ground with the total number of touches recorded. Two trials were completed, with a maximum of 100 touches per attempt permitted, allowing a maximum number of 200 touches. Each player completed these tests in an indoor sports hall with a hard-wood floor, with generic training kit being worn. In addition, age group-specific balls were used for the tests in-line with the Football Association regulations, with size three for under-9, size four for under-10 to under-13, and size five for under-14 to under-16 [27,28].

#### Eight Match Analysis Statistics from across the Entire Season

Video footage examined each player during competitive match-play as they performed each skill behaviour. An average score of each skill behaviour was computed from across an entire football season, including: (a) reliability in possession percentage, (b) pass completion percentage, (c) number of tackles, (d) number of blocks, (e) number of loose balls retrieved, (f) successful dribble completion, (g) total touches, and (h) goals scored. As a standard pro-forma of match analysis statistics within each academy varies based on its philosophy, this current study applied the academy's existing protocol for its data collection. The specialist software Gamebreaker© was used to perform participant analysis for each game and trained, club-appointed Performance Analysts (who were not part of the research team and were blind to the grouping of the study participants) adopted technical expert definitions to code behaviours (*n* = 10). Twenty matches (25% of the data) of the matches that were included in the current study were used to calculate the Performance Analysts' reliability (15-day test–retest analysis). One match per team was randomly selected to carry out the intra- and inter-reliability analysis. An intra-class correlation coefficient test was executed to analyse the reliability levels (poor, <0.50; moderate, 0.50 to 0.75; good, 0.76 to 0.90; excellent, 0.91 to 1.00). Results showed the intra-observer reliability ranged from 0.76 to 1.00 and the inter-observer reliability ranged from 0.71 to 1.00 [28].

#### Three Perceptual-Cognitive Expertise Video Simulation Tests

Film-based simulation tests were applied to examine the players' decision-making skill. Action sequences were selected from live football match footage of academy players aged 18 to 19 years engaging in a competitive game, filmed from an elevated angle above and behind the goal. Following general build-up play of five to ten seconds in duration, the clips unexpectedly occlude immediately prior to a critical decision moment. At this point, an occlusion display appears that shows the pitch lines (i.e., boundaries, eighteen yard box, and half way line) and the location of the ball on a white screen. This screen was frozen for 7-s whereby the participant had to select their answer on the response sheet before the next clip automatically begins. Forty-five clips were created for three different phases that were used for analysis: (a) 'pre', (b) 'at', and (c) 'post' execution. Thus, 135 clips were viewed by the players in total. 'Pre' clips are considered more difficult as the occlusion happens 0.5 s

prior to the action that is executed, whereas the 'at' clips occlude during the moment the action is executed, as opposed to the 'post' clips that are considered the easiest as they are occluded after the execution with a duration 0.5 s longer. Consequently, clips are viewed in this order, with a response sheet completed separately and collected before the next batch of clips begin, to prevent players changing their answer when they see the longer clips. The 45 film-based simulations are distributed into three decision-making skills, including 'select action', 'select direction', and 'select pass recipient', thus creating 15 clips for each [29].

#### Eight Anthropometric Measures

The physiological measures included: (a) height, (b) body mass, (c) body mass index (BMI), and (d) body fat percentage. Height measures were recorded to the nearest 0.1 cm (Seca 213 Leicester Height Measure). Body mass measures were recorded to the nearest 0.1 kg (Tanita BF-350 Body Composition Monitor). Body mass index was calculated through dividing weight (kg) by height (m) and dividing that number by height (kg/m2 ). Body fat percentage was also estimated (Tanita BF-350 Body Composition Monitor). Players completed these procedures bare footed with their training shorts and t-shirt on. Moreover, the Khamis-Roche method was used to analyse: (a) predicted adult height, (b) percentage of predicted adult height attained, and (c) PHV status. The Khamis-Roche method is based on a mathematical calculation using the child's gender, current height and body mass, and the height of both parents. The formula applied to predicted height in inches is: =((age factor) \* (age in years)) + ((height factor) \* (height in inches)) + ((body mass factor) \* (body mass in pounds)) + ((parental height factor) \* (parental height in inches)) + (beta coefficient) [35]. The participants predicted adult height then identifies the percentage of predicted adult height attained. Additionally, the growth curve attained from this data identifies the participants PHV status: (a) pre-, (b) circa-, and (c) post-PHV. Lastly, birth quartile was measured by dividing the twelve months of the year into four quarters, conforming to the strategy applied to distribute chronological age groups. Due to the start of the section year beginning in September in England, this is recognised as 'month 1' while August is 'month 12'.

#### Eight Fitness Tests

Fitness tests were conducted with the participants to measure specific physical parameters, including acceleration, sprint, agility, and jump abilities. These tests were executed by the first author and have been proved valid and reliable measures for talent development research in youth football. Players were already familiarised with these testing procedures since they were already part of the academy fitness testing protocol. The 0–30 m sprint test started 1 m behind the first set of timing gates (Brower TC Timing System, Draper, UT, USA). Participants sprinted until passing the final set of timing gates. Timings for 0–10 m, 0–30 m, and 10–30 m were taken to observe acceleration and sprint speed, respectively. The L-agility test required the participants to start 1 m behind the first set of timing gates (Brower TC Timing System, Draper, UT, USA), then run forwards 5 m around the tall centre cone, run 5 m to the left hand cones and place one foot between the two marker cones, and then turn and follow the same path back to the start. In the second trial, players performed the same test, but this instance running 5 m to the cones on the right-hand side. Timings were recorded for the right, left, and combined. During the CMJ test (Just Jump system, Probotics Inc. 8602 Esslinger CT, Huntsville, AL, USA), players were instructed on the importance of using a countermovement and the need to take-off and land with straight legs, with the jump height (cm) and time (s) recorded for analysis. Three trials were completed for each test with the best result taken for investigation. Players conducted these fitness tests in a sports hall, whilst players completed a familiarity session prior to the data collection to counteract any earning effects.

Six Factors from the 59-Item Psychological Characteristics for Developing Excellence Questionnaire

The 59-item PCDEQ was used to assess psychological characteristics across six dimensions. Each of the questionnaire's items is placed on a six-point Likert scale with a similarity response method from '1' (very unlike me) to '6' (very like me). This ensured participants were not allowed to remain neutral and therefore encouraged them to think more carefully about whether they agree or disagree with the statement leading to greater accuracy. Additionally, a mixture of positively and negatively worded items is included to minimise the danger of acquiescent bias. The PCDEQ is designed for youth athletes, thus offers user-friendly language that is applicable to this cohort (see MacNamara and Collins [31] for the psychometric properties of the PCDEQ). The participants completed the PCDEQ in a classroom setting. They were allocated 45-min to complete it and the researcher was available to help answer any questions if the participants were unsure.

#### Ten Items from the Participation History Questionnaire

The PHQ is a retrospective recall questionnaire, which is used to elicit information regarding the activities in which players have engaged in during their development. The test–retest reliability and the concurrent validity of the PHQ have been previously established by Ford and colleagues [33]. The PHQ contains three sections including milestones within football, engagement within football activities, and engagement in other sport activities. Initially, the football-specific milestones include both: (a) the age at which the player first engaged in football, and (b) the age they began participation in a professional football academy. The second section of the PHQ is designed to elicit information from four football-specific activities: (a) match-play, (b) coach-led practice, (c) individual practice, and (d) peer-led play. The hours per week and months per year in each of these football activities, as well as the accumulation of time spent engaged in all of these activities, were recorded in the PHQ for each year from the current season back to the year the participant stared playing football. Finally, the third section of the PHQ is designed to produce information concerning engagement in other sport activities, including: (a) total sports played, and (b) total multi-sports hours. It contains a list of sports from which players were required to indicate those in which they have participated in regularly for at least a total minimum period of three months. Players were not required to record other sport activities engaged in during Physical Education (PE) classes in school. Total football and multi-sport hours were also included as a measure. The participants completed the PHQ in a classroom setting. Each participant was given one hour to complete the PHQ under supervision from the lead author, while allowing questions to facilitate individual understanding.

#### Six Measures from Postcode Data

Social classification and credit score are proxy indicators of socioeconomic status. In the UK, postcodes are associated with data pertaining to the locale to which they correspond. These data include income, employment, education, health, and crime levels, which can be accessed in multiple ways. For this study, the UK General Registrar Classification system was adopted that uses the average credit rating applying the Cameo™ geodemographic database. This provided a social classification (A, B, C1, C2, D, and E) determined by the UK's Office for National Statistics and an average credit score (out of 999) for where each participant lives and goes to school. The social classification was scored numerically, with a higher score relating to a lower social classification (i.e., A = 1, B = 2, C1 = 3, C2 = 3, D = 4, and E = 5). The credit score denotes those with a higher score to have lower financial risk from '0' (low) to '999' (high). The participants area code was also included to test outline whether they are from urban and rural settings.

#### 2.1.3. Player Review Ratings

Player profiling is a widely used tool that is utilised within professional football academies [36]. Indeed, coach opinion is central to the subjective nature of youth football, with modern objective information readily available to professional coaches to support their judgement [37,38]. This study applied a unique progress assessment to measure each individual's development. This tool, named the *43 Progression Steps*, applies a holistic approach during the player review process. This includes capturing the club's pre-existing philosophy of developing core skills within *mental*, *physical*, *technical*, and *tactical* variables. These four sub-components grade specific characteristics that are considered necessary for development and progress towards senior professional status within this particular football club. The scoring system for the player profiling reports has a continual and progressive pattern rather than identical Likert scales. For example, the under-9 rating scale ranges from 1 (below average) to 4 (excellent), while the under-16 rating scale ranges from 26 (significantly below the required standard) to 33 (pushing towards the under-18 s). Throughout the development process, these specific grades are not prescribed within age groups, with players able to move through the tool seamlessly if they are developing or playing in certain areas above or below their chronological age.

The player review ratings were initially completed by the players who give their perception of themselves, and then the coaches subsequently provided their ratings alongside specific individual learning objectives. These reports were completed three times (i.e., pre-season, mid-season, and end of season), with each coach having completed the participants' review ratings throughout the two seasons included in this study across the under-9 to under-16 age groups. Only the accumulated scores for all the components within each participant's 43 Progression Steps rating were recorded at the start of season one and the end of season two in order to create two time points and analyse year-on-year developmental outcomes. Comparing the differences between the overall scores from the two player review ratings illustrated each player's total development over two years, which was the score used for the data analysis in this current study. Two coaches from each age group (*n* = 16), who were deemed suitably qualified assessors (UEFA Pro, 'A', or 'B' Licenced alongside either the FA Advanced Youth Award or the FA Youth Award), graded each participant's player review ratings for each of the specific characteristics. See Kelly's doctoral thesis [34] for a comprehensive overview of the 43 Progression Steps player review tool.

#### 2.1.4. Data Analysis

The dataset was analysed via Lasso linear regression using cross-validated Lasso regression as implemented in the glmnet package in R [39]. Analysis of the improvement in player score across the two seasons used a coach assessed outcome measure, with the scores standardized at an age group aggregate:

$$y\_{i,t} = \frac{\mathbf{x}\_{i,t} - \mu\_t}{\sigma\_t}$$

where *yi*,*t* is the corrected scores for the *i*th member of age group *t*, *xi*,*t* is the uncorrected scores for the *i*th member of age group *t*, *µt* is the mean of the *xi*,*t* scores and *σt* is the standard deviation of the *xi*,*t* scores. The independent variables (IVs) were divided into categorical ("Home Postcode Social Grade", "School Postcode Social Grade", "PHV Status", and "Birth Quarter") and numeric (see SI for full list). Each numeric IV was standardized for mean at standard deviation at an age group aggregate, and the categorical IVs underwent a one-hot vector encoding [40]. Hence, coefficient estimates for numeric IVs reflect the change in DV per standard deviation from the average, while categorical IVs reflect the change in DV where the variable possess the relative value. The cross-validation technique first learned a model penalisation parameter, λ, by optimising the model performance characteristic (mean squared error) under 10-fold cross validation. The results for the optimal value of λ were then extracted to identify key contributing factors towards player review ratings.

#### *2.2. Results*

The summary of the Lasso regression techniques is outlined in Table 1. Results showed non-zero coefficients for improvement in subjective performance in 15 out of the 53 analysed features. Key findings revealed advanced percentage of predicted adult height (0.196), greater lob pass (0.160) and average dribble completion percentage (0.124), more total match-play hours (0.145), and an older relative age (BQ1 vs. BQ2: −0.133; BQ1 vs. BQ4: −0.060) were the most important features that contributed towards player review ratings.

**Table 1.** Summary of non-zero coefficients for improvement in subjective performance.

#### *2.3. Discussion*

The purpose of this exploratory study was to examine the multidimensional factors that contributed to player review ratings across two seasons by applying a machine learning approach. Results showed a total of 15 of the 53 analysed features were important contributors towards player review ratings, which were representative of all four subcomponents from the FCM (i.e., technical/tactical, physical, psychological, and social). Most notably, advanced percentage of predicted adult height, greater lob pass and average dribble completion percentage, more total match-play hours, and an older relative age were the largest features. Taken together, these findings underscore the holistic nature of the talent development processes in youth football.

Advanced percentage of predicted adult height had the greatest influence on player review ratings. The variation of maturation status (i.e., early, on-time, and late) between players within a single chronological age group can lead to up to 5-years difference in biological age [41]. The trainability and performance of physical competencies are closely aligned with maturity status [42]. Male players who experience their adolescent growth spurt mature earlier than their peers are invariably taller and heavier from late childhood and possess greater absolute and relative lean mass [43–45]. As a result of their advanced maturity, early maturing players also tend to outperform their less mature peers on tests of strength, power, speed, agility, and endurance [45,46]. However, from a psychological perspective, Cumming and colleagues [47] showed how later maturing players are more likely to possess and/or develop more adaptive self-regulation skills in the long-term, in particular self-evaluation and reflection. Moving forward, coaches should reflect on how an advanced maturity status can influence football-specific developmental outcomes from a holistic perspective (e.g., technical/tactical, physical, psychological, social). Since maturity status can significantly influence football-specific skills (e.g., physical competencies, selfregulation), coaches should observe and/or review players based on their maturity status

(e.g., bio-banding [48]), rather than just their chronological age. This would support the long-term development of a wider pool of potential talent and focus on retaining later maturing players, whilst move the focus on short-term performance results that largely benefit early maturing players [25].

Greater lob pass and average dribble completion percentage (technical), as well as PCE 'at' and PCE 'post' (tactical), were important contributing features towards player review ratings. Coaches are the decision-makers in the player review rating process and often have a greater understanding of technical/tactical features compared to the other subcomponents of the FCM. Thus, it is not surprising that technical/tactical skills featured within these current results, as it is possible that greater value may be placed on these characteristics compared to the other subcomponents. This is emphasised by the traditional coach education and sport-specific qualifications that often focus on athlete competence compared to other developmental factors (e.g., confidence, connection, and character [6]) [49]. As such, although further evidence is needed, it is suggested coaches and organisations involve other stakeholders (e.g., Sport Scientists, Sport Psychologists, Strength and Conditioning Coaches) as part of a broader, holistic decision-making strategy when reviewing young players development [50].

There has been an ongoing search for the most appropriate activities that facilitate long-term player development in youth football (see Ford & Williams [51] for an overview). Findings from this current study found more total match-play hours had the largest contribution towards player review ratings. This may be explained through the coaches who are providing the players with their review rating being the same coaches who are selecting the players for the matches, and thus may be offering the players they perceive as better with more game time. However, it's important to note that these coaches would have only been responsible for selecting these players in the recent years, whereas the *total* match-play hours accumulate the numbers the player has engaged in since they began playing football. Thus, another possible explanation is the benefits of engaging in match-play that may have contributed to player development. For instance, small-sided games have been shown to develop and refine young players' skills and movements [52–54]. As such, coaches should consider how to offer a rich games programme to their players, through both competitive match-play and small-sided games, which could contribute to the holistic development of young players.

Birth quarter played an important role in influencing player review ratings, which favoured those born in the first three month of the year. This aligns with a wealth of relative age literature in youth soccer. As an example, initial research from Barnsley and colleagues [55] showed 45% of players selected for the 1989 U17 World Cup were born in the first three months of the annual selection year, whereas only 7.7% were born in the last three months of the annual selection year, with similar results shown across the U20 team squads. Since this preliminary research, the last three decades has generated various studies that shows how those born earlier in the selection year are overrepresented in talent pathways [56], accrue more league points [57], and win more games [58]. However, these benefits at youth level do not necessarily translate into success at adulthood in professional football [59–61]. As such, it is plausible to suggest that coaches perceive greater development in those who are relatively older largely due to their advanced age. As such, it is important for future research to explore the mechanisms of relative age effects and how they impact coaches perceived potential. Practitioners and researchers should also work collaboratively to design, implement, and evaluate a range of relative age solutions to help mitigate against these effects.

#### **3. The Junior-to-Senior Transition from Youth Academy to Professional Level: Exploring the Characteristics of Selected and Deselected under-18 Players**

Becoming a professional footballer is the aspiration of many academy prospects. However, it is well documented that only a small proportion of young players successfully graduate into senior professional levels. As an example, Dugdale and colleagues [62]

showed how only 10% of 537 male players made the successful transition to professional level across a twelve-year period at a Scottish professional football club. Similarly, spanning an eleven-year period at an English professional football club, Kelly and colleagues [63] revealed how only 7.4% of 364 male players who entered the academy from under-9 to under-18 achieved a professional contract at aged 18 years. To better understand the juniorto-senior level transition, it is important to consider the characteristics that differentiate those academy players who achieve professional status and those who do not. By doing so, it will enable key stakeholders (e.g., coaches, practitioners, policy makers) employed in talent development programmes to allocate resources more efficiently, as well as facilitate a science-based support system [25].

The purpose of this study was to examine a range of factors based on the FCM (i.e., technical/tactical, physical, psychological, social) that may have contributed to under-18 academy football players achieving a professional contract.

#### *3.1. Methods*

#### 3.1.1. Sample

Eighteen under-18 male participants were recruited from the same tier four English professional football club and their category three academy. Their average weekly training and match-play time was 15 training hours/week and 1.5 match-play hours/week. Goalkeepers were not included in this study due to their contrasting position-specific requirements [26]. Parental consent and player assent were collected prior to the study commencing. The study was approved by the Ethics Committee of Sport and Health Sciences at the University of Exeter.

#### 3.1.2. Measures and Procedures

The same 53 factors from the eight measures outlined in Study 1 were collected for this study across two seasons (2014–2015 and 2015–2016). Player review ratings were also added since they were not used as an outcome measure. This dataset was then used to compare selected (i.e., offered a professional contract; *n* = 8) and deselected (i.e., not offered a professional contract; *n* = 10) players as they reached the end of their academy scholarship.

#### 3.1.3. Data Analysis

The dataset was analysed via Lasso regression techniques using cross-validated Lasso regression as implemented in the glmnet package in R. Analysis of the 'Selection' for professional play was performed using binomial Lasso regression, coding the outcome as 1 for 'Selected' and 0 for 'Deselected'. The cross-validation technique first learned a model penalisation parameter, λ, by optimising the model performance characteristic (binomial deviance) under 10-fold cross validation. The results for the optimal value of λ were then extracted to identify key contributing factors. In reporting the results of the binomial Lasso regression, the exponential of the coefficients was included. In the case of a logistic binomial mode, the exponential of the coefficients is equivalent to the change in odds ratio for each increase of the dependent variable by 1, one standard deviation in this case [13,40].

#### *3.2. Results*

The summary of the Lasso regression techniques are outlined in Table 2. The relatively small parameter space of importance in Table 2 is not indicative that few features matter, but instead due to the limited quantity of data available. The size of the effect of the psychological factor, while arising from a limited quantity of data, should be noted. Having included the possibility for multiple confounding factors, the strongest marker for signing was the psychological outcomes of the player. What is not clear, given the observational nature of the study, is if improvements in psychological factors would lead to a greater chance of signing a professional contract or if it is in fact a proxy variable marking out players with a range of sought after factors.

**Table 2.** Summary of non-zero coefficients for likelihood of signing a professional contract.

The prominence of Factor 3 (coping with performance and developmental pressures) with regard to both end points posed a key question: is this factor more prominent than the other five factors, or are all the six closely correlated in the dataset and the Lasso is selecting the most informative? To quantify the relative associations of the six PCDEQ factors, the correlation matrix was calculated for the progression data set. Of the PCDEQ factors, Factor 3 only shows a reasonably strong correlation with Factor 4 (ability to organise and engage in quality practice), with only weak links to the other terms. Hence, we conclude that PCDEQ Factor 3 and Factor 4 are the strongest discriminatory variables relating to signing a professional contract. Hence, it appears valid to suggest Factors 1, 2, 5, and 6 may pose no contribution to signing a professional contract.

#### *3.3. Discussion*

The junior-to-senior transition is arguably the most defining moment in a promising young player's career. Indeed, by achieving their first professional contract, a player moves one-step closer to fulfilling their aspirations of competing for their respective senior first team. To the author's knowledge, this was the first study to explore the characteristics of selected and deselected under-18 academy players using machine learning techniques. Key findings revealed how PCDEQ Factor 3 and Factor 4 were important contributing factors towards achieving a professional contract. Moreover, player review ratings (i.e., higher coach scores), slalom dribble (i.e., quicker dribble times), and a lower home social classification (i.e., derived from more deprived areas) also provided a small contribution.

PCDEQ Factor 3 and Factor 4 were important contributing factors within this current study. Indeed, psychological factors have been previously identified as important attributes that are required during the junior-to-senior transition. As an example, the current findings are consistent with previous studies that found 'good developers' within team sports had a significantly greater perceived ability to cope with performance and developmental pressures (e.g., such as overcoming struggles, set-backs, injury, or a decline in performance) compared to 'poor developers' [32]. These current findings also compliment the opinions of coaches as derived from qualitative studies. First, Mills and colleagues' [64] analysis of ten expert coaches revealed six factors, including resilience, that were perceived to either positively or negatively influence player development. Second, Cook and colleagues [65] reported four general dimensions of mental toughness, including competitiveness with self and others, mind-set, resilience, and personal responsibility, that are associated with the ability to cope with the pressures inherent in the academy environment. Similarly, Holt and Mitchell [66] identified a deficiency in coping behaviours of professional football players near to being released, whereas Holt and Dunn [67] revealed how discipline, commitment, resilience, and social support were associated with becoming a professional football player. While it is plausible to suggest that these psychological characteristics are generally accepted as crucial factors for positive developmental outcomes, further investigation is required to design, implement, and evaluate effective psychological development strategies within academy environments [68]

When compared to other specialist support staff in youth soccer environments (i.e., Sport Scientists, Strength and Conditioning Coaches, Performance Analysts), Sport Psychologists appear to be less common (particularly in a full-time capacity) [68]. Since the development of psychological characteristics appears to be an important contributing factor towards both coaches perceived development outcomes (i.e., Study 1) as well as achieving a professional contract (i.e., Study 2), professional clubs and governing bodies should consider how they can formalise their psychological support and invest in qualified practitioners. By doing so, it will enable young players the opportunity to access psychological support when required, as well as help with coach development to ensure effective strategies are consistently implemented throughout coaching provision [69].

Unsurprisingly, player review ratings contributed to selection. This is likely due to the fact that those coaches who are rating the players are the same stakeholders who are part of the professional contract decisions. More unexpectedly, dribbling ability and social classification also made a small contribution towards selection. First, dribbling has been previously identified as an important technical attribute as part of a multidisciplinary study when comparing selected and deselected players [2]. Thus, these current findings further support the significance of possessing ball dribbling skills, which could be incorporated into developmental programmes to ensure players are adequately prepared as they navigate their ways towards senior levels. Second, the results of a lower home social classification contributing to achieving a professional contract reflect the stereotype of football being a sport participated by individuals with a lower socioeconomic status [70]. This might imply that football retains a traditional divide between socioeconomic status and participation [71], which may have implications on opportunities to achieve a professional contract [72]. However, it is important to consider the exploratory nature of these findings, as well as the limited number of participants included in this current study. Therefore, future research is encouraged to further explore the significance of possessing ball dribbling skills and the role of socioeconomic status in developing expertise in football.

#### **4. Limitations**

The key limitations of this study are the role of retrospective analysis and predictive models. The techniques used here are best viewed as exploratory and hypothesis generating, rather than confirmatory, as they do not seek to provide evidence for or against any preexisting mechanisms but generate new insight and an optimal predictive model given the available data. In the case of the 'Selection' analysis, the inherently small dataset does provide limitations to the analysis, whereby machine learning approaches with small datasets inherently run the risk of memorising the sample rather than generating transferable lessons [73]. In general, the subset selection algorithms demonstrated here could provide stakeholders within the football development community with insight into the operational data currently held. Data collection and storage mechanisms have increasingly become cheaper over the past two decades (e.g., the rise of wearable technology and cloud storage). Moving forward, an important question is how to leverage such data to aid decision making. Techniques such as those shown here are invaluable in being able to quickly and easily reduce data to interpretable models and highlight key signals.

#### **5. Conclusions**

It appears the key factors associated with positive developmental outcomes in youth soccer are not always technical and tactical in nature, which is where youth coaches often have their expertise and/or focus their attention on talent development. Indeed, the relative importance of these factors is likely to change over time, and with age, although psychological attributes appear to be influential to reaching potential across the academy journey. Therefore, coaches are encouraged to focus on long-term potential as opposed to short-term performance. The techniques used here also serves as an impetus for researchers to adopt machine learning approaches when analysing multidimensional databases for talent development purposes.

**Author Contributions:** Conceptualization, A.L.K.; Data curation, A.L.K.; Formal analysis, R.C.; Methodology, R.C.; Software, R.C.; Supervision, C.A.W. and M.R.W.; Writing—original draft, A.L.K.; Writing—review and editing, C.A.W., S.L.J.S. and M.R.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was co-funded by the University of Exeter, College of Life & Environmental Sciences, the Open Innovation Platform at the University of Exeter, and Exeter City Football Club Academy. Thanks to the players, parents, and staff at Exeter City Football Club Academy for their participation and support in this project.

**Institutional Review Board Statement:** The study was approved by the Ethics Committee of Sport and Health Sciences at the University of Exeter. Reference number: 141203/B/15.

**Informed Consent Statement:** Written parental consent and player assent were collected prior to the study commencing.

**Data Availability Statement:** Data can be obtained via the lead author.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


*WU-ABC Optimization*

In this study, we propose a solution through the WU-ABC algorithm, a hybrid of the weighting update method and the ABC algorithm, to solve the CEED optimization problem. The ABC algorithm is a swarm-based optimization method that simulates the foraging behavior of a bee swarm; it has recently been used in many studies to solve optimization problems [36,37]. However, it is difficult to solve the proposed CEED problem under emission constraints using the ABC algorithm alone. Therefore, the weighting update method is also employed to solve the emission problem by adjusting the penalty factor and EBDR capacity. The WU-ABC algorithm involves five main steps, as described below:

• *Initialization step*: In this step, the initial population of food source is placed randomly in a D-dimensional problem space.

$$\mathbf{x}\_{nm} = \mathbf{x}\_{nm}^{\min} + rand[0, 1] \cdot (\mathbf{x}\_{nm}^{\max} - \mathbf{x}\_{nm}^{\min}) \tag{35}$$

Equation (35) represents the *m*th random food source of dimension *n* in the CEED problem.

• *Employed bees step*: Each bee repeatedly explores a food source to find the optimal solution, and then it chooses a new optimal position (*vmn*) close to the reference position.

$$
\omega\_{mn} = \mathbf{x}\_{mn} + \phi\_{mn} \cdot (\mathbf{x}\_{mn} - \mathbf{x}\_{an}), \quad m \neq o \tag{36}
$$

When an employed bee finds a food source that is better than the reference one (*xmn*), the reference food source is superseded with the new candidate.

• *Onlooker bees step*: Onlooker bees look for new positions that are close to the old position through a greedy search method. The bees consider a fitness value, the amount of nectar, and probabilistically determine the space for their next exploration, as follows:

$$P\_m = \frac{fit\_m}{\sum\_{m=1}^{SN} fit\_m} \tag{37}$$

In Equation (37), *fitm* is a value that is proportional to the amount of nectar.

$$h(s+1) = h(s) \times \exp\left[\eta \times \left(\frac{em(s) - em^{\max}(s)}{em^{\max}(s)}\right)\right] \quad \text{if} \quad cm(s) > cm^{\max}(s) \tag{38}$$

After updating the penalty factor using Equation (38), optimization is performed again through the ABC algorithm.

Figure 3 summarizes the proposed CEED solution. The procedure is performed sequentially as follows:

**Figure 3.** Overall process of the combined economic emission dispatch (CEED) solution.


*4.2. Passive Methods*

Compared to the communication-based methods, the passive islanding detection methods present simple implementation based on protection relays and synchronization systems [41,49]. The most famous passive methods are over/under voltage (OUV) and over-under frequency (OUF), phase jump (PJ), rate of change of frequency (ROCOF), voltage harmonic monitoring (VHM).


**3.5 Chronic tension type headaches**

In two studies of one group of patients (Ashina et al., 2002; Ashina et al., 2003) with chronic tension headaches, no differences were found in metabolites and some algesic substances (e.g., BKN and PGE2) in the trapezius muscle (**Table 5**).


**4. Methodology**

The MODIS global disturbance index (MGDI) was developed based on the concept that any perceptible disturbance of ecology will result in a significant alteration in vegetation and a concomitant change in the land surface temperature [14, 16].

In the present study, the flood and forest fire were selected as the causative factors that create ecological disturbance to address both the non-instantaneous and instantaneous disturbance, respectively. In case of instantaneous disturbance like forest fire, the disturbance is manifested immediately after the event, resulting in immediate increase in LST with decreased vegetation cover. On the contrary,

#### *Remote Sensing*

non-instantaneous disturbance like flood will not trigger immediate change in either LST or EVI due to availability of abundant moisture for evaporation to offset the loss of transpiration. Whereas, in the following year the effect of flood damage was evident due to vegetation mortality and severe structural damage, which will eventually lead to increase in annual maximum LST due to the reduction in transpiration [14].

The instantaneous (*MGDIinst*) and non-instantaneous (*MGDInon-inst*) MGDI were computed using the following equations:

$$\text{MGDI}\_{\text{out}} = \frac{\left(LST\_{\text{max}} \; / \, EVI\_{\text{max} \cdot \, post} \right)\_{\text{current } year(y)}}{\left(LST\_{\text{max}} \; / \, EVI\_{\text{max} \cdot \, post} \right)\_{\text{mid} \, time \cdot year \, mean(y - 1)}} \tag{1}$$

$$\text{MGDI}\_{\text{non-int}} = \frac{\left(LST\_{\text{max}} \mid EVI\_{\text{max}}\right)\_{\text{current } j \text{acv}(y)}}{\left(LST\_{\text{max}} \mid EVI\_{\text{max}}\right)\_{\text{multi-} y \text{acv } m \text{acn}(y-1)}}\tag{2}$$

Where, *MGDIinst* and *MGDInon-inst* are the instantaneous and non-instantaneous MGDI value, respectively. *LSTmax* and *EVImax* are the annual maximum 16-day composite LST (°C) and EVI, respectively. *EVImax*-post is the maximum 16-day composite EVI following the LST*max*, current year (y) is the year being evaluated for disturbance and multi-year mean (y − 1) is the mean of the ratios excluding the current year [14, 16].

A two-step methodology, as explained by Dutta et al. [16], was adopted for to discriminate the disturbed forest areas caused due to flood and forest fire. In the 1st step, the % change in MGDI values were calculated based on the time-series data for each pixel. Whereas in the second step the MGDI based thresholds were estimated for flood and forest fire, separately. The Percentage change in MGDI for both the instantaneous and non-instantaneous disturbance were calculated using the following equation:

$$\text{96:change in MGDI}\_{\text{current year}(y)} = \frac{\text{MGDI}\_{\text{current year}(y)}}{\text{MdLit} - year \ mean \text{ } MGDI\_{(y=1)}} \times 100 \tag{3}$$

As discussed earlier, the forests prone to flood and fire were selected based on the temporal occurrence of the natural disturbance. Total 16 representative forests frequently affected by flood and forest fire were extensively analyzed to develop the thresholds for flood and forest fire separately based upon the % change of MGDI over multi-year mean. The spatio-temporal variation of the % change in *MGDInon-inst* over the Assam forest area was shown in **Figure 4** for some selected years.

The year-wise % change in MGDI was generated for all the representative forests wherein only the flood affected pixels were considered. Similarly, year-wise % change in MGDI was generated for the pixels undergone forest fire. The temporal profile of the percent change in MGDI of each forest was compared with the area weighted flood and fire intensity, to confirm the effect of natural disturbances on the MGDI. The multi-year mean value plus one standard deviation of the % change in MGDI was considered to be the threshold, and the value was used for

*Assessment of Ecological Disturbance Caused by Flood and Fire in Assam Forests, India… DOI: http://dx.doi.org/10.5772/intechopen.94282*

**Figure 4.** *Percent change in MGDInon-inst over the Assam forest area.*

discrimination of the disturbed pixels [16]. Due to the slow and gradual impact of flood, unlike forest fire, a lower disturbance threshold was estimated in case of flood. The % change of MGDI greater than 7% and 11% of the temporal mean was fixed for discriminating the non-instantaneous (flood) and instantaneous (forest fire) disturbed pixels.

The selected thresholds were applied on the % change MGDI images (both non-instantaneous and instantaneous, separately) for identifying the year-wise disturbed forest areas. The year-wise % disturbed area was estimated for each forest and the temporal profiles were used to analyze the disturbance intensity at spatiotemporal scale. Upon integration of the year-wise disturbed area, disturbance prone maps were generated for both instantaneous and non-instantaneous events.


**3.5. Definitive treatment**

Management of acute missile-penetrating SCI is multidisciplinary. The treatment is guided by many factors, but first and above all, the patient's respiratory and hemodynamic stability are defined by the ATLS guidelines. A hemodynamically unstable patient, whose primary resuscitation has failed, should be transferred immediately to angiography or surgery suite without further delay. In a stable patient, treatment should be guided by the presence of other factors such as neurological status, mechanical stability of the spine, CSF leak, risk for infection, and other systemic injuries.


**Author details**

R. S. Bello1\*, C. N. Ezebuilo1 , K. A. Eke1 and T. A. Adegbulugbe2

\*Address all correspondence to<EMAIL_ADDRESS>
1 Federal College of Agriculture, Ishiagu, Nigeria

2 Federal College of Agriculture, Moor Plantation, Ibadan, Nigeria

#### **References**

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[3] Gan, G. A parametric study of Trombe wall for passive cooling of buildings. Energy

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[5] Bello, R. S. and Odey, S. O. Development of hot water solar oven for low temperature thermal processes. Leonardo Electronic Journal of Practices and Technologies 2009: No. 14, 73-84 Romania. ISSN: 1583-1078. URL: www.lejpt.academicdirect.org/

[6] Bello, R. S., Odey, S. O., Eke, K. A., Mohammed, A. S., Balogun, R. B., Okelola, O. and Adegbulugbe, T. A. Application of Asphalt bonded solar thermogenerator in small scale agroforesty based industry. In Elisha, B. Babatunde (Ed). Solar Radiation. Reije‐ ka: InTech 2012. pp 459-484. ISBN: 978-953-51-0384-4. URL:http://www.intechop‐

[7] Hirunlabh, J. New configurations of a roof solar collector maximizing natural ventila‐

[8] Bansal, N.K., Mathur, J. and Bhandari, M.S. Solar chimney for enhanced stack venti‐

[9] Ong, K. S. A Mathematical Model of a Solar Chimney. Monash University Malaysia

[10] Alter, L. The Trombe wall: Low tech solar design makes a comeback 2011. available at:http://www.treehugger.com/sustainable-product-design/the-trombe-wall-low-

[11] Khedari, J. Ventilation impact of a solar chimney on indoor temperature fluctuation and air change in a school building. Energy and Buildings 2000: Vol. 32 No. 1, pp.

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46150 Petaling Jaya, Malaysia: 2001.

**Photo-Degradation & Instrumentation**


**Meet the editors**

Dr. Ma Lei-Ming (PhD from Nanjing University, China) is currently a professor of Shanghai Meteorological Center and also a joint professor of East China Normal University in Shanghai, China. He is also a member of Numerical Weather Prediction Committee of Chinese Meteorological Society. Dr. Ma has published on a range of topics such as numerical weather prediction, tropical cyclones, and atmospheric

physics. A convective parameterization scheme proposed by Ma Lei-Ming (Ma and Tan, 2009) was introduced into the US NCAR/WRF numerical model (V3.3) and was verified to be effective in improving the false alarm problem of the original Kain-Fritsch scheme. Prof. Ma Lei-Ming has been granted with several important academic prizes, including China Excellent Young Staff Awards on Meteorological Science and Technology (2006 and 2010), China Meteorological Administration (CMA) Awards on Meteorological Science and Technology, and Shanghai Science and Technology Award, etc.

Dr. Chang-Jiang Zhang (PhD from Beijing Institute of Technology, China) is currently a professor and master supervisor of the College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, China. He is the Second Level of New Century 151 Talent Project of Zhejiang Province, China. He is an outstanding class teacher of Zhejiang Normal University (2010), a middle-aged

discipline leader of Zhejiang Normal University (2008), and an outstanding middle-young-aged teacher (2004). He is also a member of the Chinese Image and Graphics Society and the Chinese Meteorological Society. Dr. Zhang has published more than 80 journal and conference papers in image processing, pattern recognition, wavelet transform, multiscale geometry analysis, machine learning, and their applications in tropical cyclone center location and intensity estimation.

Dr. Feng Zhang (BS from Zhejiang Normal University; MS from Chinese Academy of Meteorological Sciences; and PhD from University of Chinese Academy of Sciences) is currently a professor in the Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China. Dr. Zhang's research focuses on atmospheric parameterization

and its application to numerical models. He has published on a range of topics, including radiative transfer theory, cloud/radiation interaction, and climate models. His current research projects pertain to the development of a new radiative transfer theory to deal with continuous cloud microphysical property and its application in climate models.

Contents

**Preface VII**

**Section 1 Introduction Chapter 1**

**Prediction 3** Lei-Ming Ma

Xiao-Qin Lu

Chapter 1 **Introductory Chapter: Understanding of Atmospheric Systems with Efficient Numerical Methods for Observation and**

Chapter 2 **Tropical Cyclone Center Determination Algorithm by Texture and Gradient of Infrared Satellite Image 9**

Chapter 4 **Rainfall Nowcasting by Blending of Radar Data and Numerical**

Chang-Jiang Zhang, Qi Luo, Yuan Chen, Juan Lu, Li-Cheng Xue and

**Section 2 Numerical Methods for Atmospheric Observation 7**

Chapter 3 **Polarization Remote Sensing for Land Observation 23**

Hai Chu, Mengjuan Liu, Min Sun and Lei Chen

Chapter 6 **Atmospheric Radiative Transfer Parameterizations 87**

Chapter 5 **Spectral Representation of Time and Physical Parameters in**

Feng Zhang, Yi-Ning Shi, Kun Wu, Jiangnan Li and Wenwen Li

Lei Yan, Taixia Wu and Xueqi Wang

**Numerical Weather Prediction 71** Kristoffer Lindvall and Jan Scheffel

**Section 3 Numerical Weather Prediction 51**

**Weather Prediction 53**


**5. Future potential of altmetrics in various fields of application**

To what extent altmetrics will establish themselves in research policy depends fundamentally on empirical values from practical application in the sense of a learning experimental system. Therefore, potential fields of application are briefly outlined in the following paragraphs.