since 2009 the international judo federation ( ijf ) established a world ranking list based on different competitions levels . before the last olympic games the athletes received a specific number of points according to their performance in the following competition : continental open , 100 points ; grand prix , 300 points ; continental championships , 400 points ; grand slam , 500 points ; world masters , 700 points ; world championship , 900 points ; olympic games , 1,000 points .
the champion received 100% of these points , while the silver and bronze medalists received 60% and 40% of these points , respectively .
as the ranking is dynamic across the season , points scored in the last 12 months are 100% valid , while points obtained in the 1324 months before a given date are reduced to 50% .
additionally , nowadays the best five results in the 24 months range plus the performance either in the continental championship or in the world masters are considered . according to the ranking position , the first eight athletes taking part in a specific competition are seeded in the draw . furthermore , the 22 and 14 best ranked male and female athletes area directly classified to the olympic games , respectively ( international judo federation , 2016 ) .
the olympic games are the most relevant competition for judo athletes ( franchini and takito , 2014 ) and the most traditional nations in this sport need to invest a considerable amount of money to maintain their athletes taking part in the main competitions of the judo world tour ( franchini et al . ,
although the ranking system has been criticized due to the home advantage effect noticed in these competitions ( ferreira julio et al . , 2013
; krumer , 2017 ) and to its low predictive value for the olympic games performance ( franchini and julio , 2015 ) , the fact that the draws are seeded has a strong influence on some countries trying to have a larger number of athletes among the top eight in each weight category .
however , little is known concerning the real influence of being seeded on final performance in the olympic games .
this information can be valuable to create strategies to optimize athletes participation in the judo world tour competitions and performance in the main judo competition ( i.e. , olympic games ) , while avoiding the risk of injury ( kim et al . , 2015 ) . additionally ,
while the first ranked athlete is positioned in the theoretically easier pool , being the number one directs much attention from the opponents as the athlete in such position is normally considered the athlete to be defeated .
thus , the main goal of the present study was to calculate the probability  based on a bayesian approach  to win a medal in the olympic games given the athlete is seeded and to verify if the number one ranked athlete has any advantage compared to other seeded athletes concerning his / her chances to be olympic champion .
data from athletes who took part in the london 2012 and rio 2016 olympic games were considered in the present study , totaling 470 male and 307 female judo athletes from seven weight categories for each sex . in these competitions
112 athletes were seeded ( eight for each weight category in each sex ) and 56 medals were distributed .
the main outcome was the probability of getting an olympic medal given the athlete was seeded ( i.e. , top eight in the world ranking list ) compared to nonseeded athletes , as well as the probability of being olympic champion given the athlete was the best ranked compared to the other seeded athletes .
thus , the seeded athletes and medal winners were identified in each specific draw during the two olympic games editions where this system was applied ( i.e. , london 2012 and rio 2016 ) .
if the initial probability p(m ) of an event m occurring is influenced by a second given event c to happen , then it is possible to calculate an a posteriori probability p(m|c ) called conditional probability .
bayesian approach uses the notion of conditional probabilities , but also updates the initial odds with new hypothesis . these new hypothesis work as weights on a weighted average . in short , bayes theorem
is stated by : for all i=1 , 2 ,  , n. ( adapted formula from bussab and morettin , 2010 ) .
the first step done by the present study was to calculate the probability p(m1|c ) of getting an olympic medal given the athlete was seeded ( i.e. , top eight in the world ranking list ) compared to nonseeded .
the following probabilities were used to calculate p(m1|c ) : p(m1 ) : initial probability of an athlete , seeded or nonseeded , winning a medal is equal to 
 rn , where r = number of medals at stake ; n = number of competitors ; 
 p(m1c ) : probability of an athlete , seeded or nonseeded , not winning a medal is equal to 1-p(m1 ) ; p(c|m1 ) : probability of being a seeded athlete given this athlete won a medal is equal to 
 wr , where w = number of seeded athletes who won a medal ; 
 p(cm1c ) : probability of being a seeded athlete given this athlete did not win a medal is equal to 
 lr , where l = number of seeded athletes who did not win a medal .
thus , the probability of getting an olympic medal given the athlete was seeded ( i.e. , top eight in the world ranking list ) compared to non - seeded was calculated by : according to the table 1 , for example , the probability of an athlete getting an olympic medal , in london 2012 , given that this athlete was seeded in the under 66-kg weight category is equal to 
 0.111 * 0.5000.111 * 0.500 + 0.889 * 0.1870.250 . the second step done by the present study was to calculate the probability p(m2|c ) of being olympic champion given the athlete was the best ranked one compared to the other seeded athletes . in this case , the probabilities were not calculated by categories , but among males and females , as well as in london 2012 and rio 2016 . to finally calculate p(m2|c ) , the following probabilities were previously found : p(m2 ) : initial probability of the best seeded athlete being olympic champion is equal to 
 os , where o = number of olympic champions ; s = number of seeded athletes ; 
 p(m1c ) : probability of the best seeded athlete not being olympic champion is equal to 1p(m2 ) ; p(c|m2 ) : probability of being the best seeded athlete given this athlete was olympic champion is equal to 
 ba , where b = number of best seeded athletes who were olympic champions ; a = number of seeded athletes who were olympic champions ; 
 p(cm2c ) : probability of being the best seeded athlete given this athlete was not olympic champion is equal to 
 de , where d = number of best seeded athletes who was not olympic champions ; e = number of seeded athletes who were not olympic champions .
thus , the probability of being olympic champion given the athlete was the best ranked compared to the other seeded athletes was calculated by : according to the table 3 , for example , the probability of an athlete being olympic champion given that this athlete was the best male seeded in his category , london 2012 , is equal to 
 0.125 * 0.2000.125 * 0.200 + 0.875 * 0.1180.195 .
the draws of each weight category for each sex and olympic games edition were retrieved from the ijf website ( http://www.ijf.org ) and these archive data are from open - access .
morley and thomas ( 2005 ) affirm that there are no ethical issues in analyzing or interpreting these data since they were obtained in secondary form and not generated by experimentation .
in addition , athletes personal identifications were replaced by a code , ensuring anonymity and confidentiality .
this process was used in previous studies analyzing competitions of the world ranking list ( ferreira julio et al . , 2013 ; franchini and julio , 2015 ; franchini et al . , 2016 ) .
data from athletes who took part in the london 2012 and rio 2016 olympic games were considered in the present study , totaling 470 male and 307 female judo athletes from seven weight categories for each sex . in these competitions
112 athletes were seeded ( eight for each weight category in each sex ) and 56 medals were distributed .
the main outcome was the probability of getting an olympic medal given the athlete was seeded ( i.e. , top eight in the world ranking list ) compared to nonseeded athletes , as well as the probability of being olympic champion given the athlete was the best ranked compared to the other seeded athletes .
thus , the seeded athletes and medal winners were identified in each specific draw during the two olympic games editions where this system was applied ( i.e. , london 2012 and rio 2016 ) .
if the initial probability p(m ) of an event m occurring is influenced by a second given event c to happen , then it is possible to calculate an a posteriori probability p(m|c ) called conditional probability .
bayesian approach uses the notion of conditional probabilities , but also updates the initial odds with new hypothesis . these new hypothesis work as weights on a weighted average . in short , bayes theorem
is stated by : for all i=1 , 2 ,  , n. ( adapted formula from bussab and morettin , 2010 ) .
the first step done by the present study was to calculate the probability p(m1|c ) of getting an olympic medal given the athlete was seeded ( i.e. , top eight in the world ranking list ) compared to nonseeded .
the following probabilities were used to calculate p(m1|c ) : p(m1 ) : initial probability of an athlete , seeded or nonseeded , winning a medal is equal to 
 rn , where r = number of medals at stake ; n = number of competitors ; 
 p(m1c ) : probability of an athlete , seeded or nonseeded , not winning a medal is equal to 1-p(m1 ) ; p(c|m1 ) : probability of being a seeded athlete given this athlete won a medal is equal to 
 wr , where w = number of seeded athletes who won a medal ; 
 p(cm1c ) : probability of being a seeded athlete given this athlete did not win a medal is equal to 
 lr , where l = number of seeded athletes who did not win a medal .
thus , the probability of getting an olympic medal given the athlete was seeded ( i.e. , top eight in the world ranking list ) compared to non - seeded was calculated by : according to the table 1 , for example , the probability of an athlete getting an olympic medal , in london 2012 , given that this athlete was seeded in the under 66-kg weight category is equal to 
 0.111 * 0.5000.111 * 0.500 + 0.889 * 0.1870.250 .
the second step done by the present study was to calculate the probability p(m2|c ) of being olympic champion given the athlete was the best ranked one compared to the other seeded athletes .
in this case , the probabilities were not calculated by categories , but among males and females , as well as in london 2012 and rio 2016 . to finally calculate p(m2|c ) , the following probabilities were previously found : p(m2 ) : initial probability of the best seeded athlete being olympic champion is equal to 
 os , where o = number of olympic champions ; s = number of seeded athletes ; 
 p(m1c ) : probability of the best seeded athlete not being olympic champion is equal to 1p(m2 ) ; p(c|m2 ) : probability of being the best seeded athlete given this athlete was olympic champion is equal to 
 ba , where b = number of best seeded athletes who were olympic champions ; a = number of seeded athletes who were olympic champions ; 
 p(cm2c ) : probability of being the best seeded athlete given this athlete was not olympic champion is equal to 
 de , where d = number of best seeded athletes who was not olympic champions ; e = number of seeded athletes who were not olympic champions .
thus , the probability of being olympic champion given the athlete was the best ranked compared to the other seeded athletes was calculated by : according to the table 3 , for example , the probability of an athlete being olympic champion given that this athlete was the best male seeded in his category , london 2012 , is equal to 
 0.125 * 0.2000.125 * 0.200 + 0.875 * 0.1180.195 .
the draws of each weight category for each sex and olympic games edition were retrieved from the ijf website ( http://www.ijf.org ) and these archive data are from open - access .
morley and thomas ( 2005 ) affirm that there are no ethical issues in analyzing or interpreting these data since they were obtained in secondary form and not generated by experimentation .
in addition , athletes personal identifications were replaced by a code , ensuring anonymity and confidentiality .
this process was used in previous studies analyzing competitions of the world ranking list ( ferreira julio et al . , 2013 ; franchini and julio , 2015 ; franchini et al . , 2016 ) .
table 4 presents the probability of seeded athletes to win medals in each olympic games . for males the probability of seeded athletes to win a medal was 41.1% and 42.9% , while for females it was 35.7% and 44.6% at london 2012 and rio 2016 , respectively .
additionally , the probability of athletes ranked as number one to become olympic champion among the seeded athletes was 19.5% and 36.8% for males and 32.3% and 36.8% for females in london 2012 and rio 2016 , respectively .
the main findings of the present study was that athletes seeded had a chance of 25.0% to 50.0% for males and between 12.5% to 50.0% for females to win an olympic medal , resulting in an overall probability of around 41.1% when all groups are considered .
moreover , being the first seeded athlete ( i.e. , ranked number one ) resulted in an overall probability to become olympic champion of around 31.0% when male and female athletes are grouped .
based on this information , it seems relevant to have seeded athletes , but being the top ranked athlete does not seem to result in an expressive advantage to become olympic champion compared to other seeded athletes . as the world ranking list is used as the main criteria to qualify to the olympic games , which is the most prestigious competition to judo athletes ( franchini and takito , 2014 ) , and the position in this ranking determines the seeded athletes in each weight category , most athletes take part in four to eight competitions during the year to guarantee their direct qualification to the competition ( i.e. , top 22 for males and top 14 for females ) ( franchini et al . , 2016 ) .
the competitions comprising the judo world tour are disputed in different countries , although the higher concentration be in europe ( ferreira julio et al . , 2013 ) .
thus , countries with limited financial resources or with high number of elite judo athletes should consider the specific cost - benefit of conducting such travels specifically to qualify their athletes to the olympic games or focusing on the seeded positions .
moreover , the fact that a high number of injuries is observed in high - level judo athletes ( kim et al . , 2015 ) , the exposure to a higher number of competition , the interval needed to increase the probability of winning ( franchini et al . , 2016 ) , the high physiological demand of judo competitions ( franchini et al . , 2013 ) , the fact that many judo athletes lose weight to compete ( artioli et al .
, 2007)and the training needed to prepare athletes to them ( franchini and takito , 2014 ; franchini et al . , 2014 )
these aspects are especially important , as the ranking position is a poor predictor of the final result during the olympic games ( franchini and julio , 2015 ) and some categories are more balanced than others ( krumer , 2017 ) .
although the number one ranked athlete is positioned in a theoretically easier pool ( as the best ranked athlete to be confronted in the semifinal is the number four , while number two may confront the number three in a semifinal ) the probability of becoming olympic champion is not so high .
this seems to happen due to the large focus that opponents rely on the top ranked athlete and to the common pressure media and supporters put on these athletes . in summary ,
the present study used a bayesian approach to determine the probability of seeded athletes to win a medal in the olympic games compared to other athletes and the probability of number one ranked athletes to become olympic champions compared to seeded athletes . as the probability of seeded athletes to get an olympic medal is around 41.1% and the probability of number one ranked athletes to become olympic champions is 31.0% , the cost - benefit of investing human and financial resources to this goal and the athlete exposure to competitions  resulting in technical - tactical analysis of the opponent and higher risk of injury  should be carefully analyzed when determining the competition calendar to each athlete .
 the main purpose of the present study was to calculate the probability  based on a bayesian approach  to win a medal in the olympic games given the athlete is seeded and to verify if the number one ranked athlete has any advantage compared to other seeded athletes concerning his / her chances to be olympic champion . for this , data from athletes who took part in the london 2012 and rio 2016 olympic games were considered . for males the probability of seeded athletes to win a medal was 41.1% and 42.9% , while for females it was 35.7% and 44.6% at london 2012 and rio 2016 , respectively . 
 furthermore , the probability of athletes ranked as number one to become olympic champion among the seeded athletes was 19.5% and 36.8% for males and 32.3% and 36.8% for females in london 2012 and rio 2016 , respectively . 
 based on these results the cost - benefit of investing human and financial resources to qualify an athletes among the top eight competitors and his / her exposure to competitions  resulting in technical - tactical analysis of the opponent and higher risk of injury  should be carefully analyzed when determining the competition calendar to each athlete . 
INTRODUCTION
MATERIALS AND METHODS
Sample
Variables considered
Bayesian approach
Ethical issues
RESULTS
DISCUSSION