This paper introduces a probabilistic method to evaluate the tactical decisions players and coaches make at the end of NBA games. For the purposes of this research, these decisions include whether to shoot a two-point or three-point field goal for the offensive team and whether to intentionally foul for the defensive team. With a win probability model built using logistic regression and player statistics, the optimal decision for both teams in a given possession is found. The End-of-game Tactics Metric (ETM) is the difference between the win probability of the optimal decision and the win probability of the actual decision. This research extends beyond current applications of win probability models to evaluate the actual on-court decision as opposed to evaluating the result of a possession. To evaluate the usefulness of ETM, the winning percentage of teams in games decided by a margin of five points or fewer can be compared with the mean ETM difference between a team and its opponent. The correlation coefficient of the relationship is -0.64. When combined with other variables that affect winning percentage in close games, a linear regression on those explanatory variables has an adjusted R2 value of 0.79. This analysis shows that the ETM difference has a significant effect on winning close games, despite having little reliance on player performance.
{"title":"Evaluating NBA end-of-game decision-making","authors":"Patrick McFarlane","doi":"10.3233/JSA-180231","DOIUrl":"https://doi.org/10.3233/JSA-180231","url":null,"abstract":"This paper introduces a probabilistic method to evaluate the tactical decisions players and coaches make at the end of NBA games. For the purposes of this research, these decisions include whether to shoot a two-point or three-point field goal for the offensive team and whether to intentionally foul for the defensive team. With a win probability model built using logistic regression and player statistics, the optimal decision for both teams in a given possession is found. The End-of-game Tactics Metric (ETM) is the difference between the win probability of the optimal decision and the win probability of the actual decision. This research extends beyond current applications of win probability models to evaluate the actual on-court decision as opposed to evaluating the result of a possession. To evaluate the usefulness of ETM, the winning percentage of teams in games decided by a margin of five points or fewer can be compared with the mean ETM difference between a team and its opponent. The correlation coefficient of the relationship is -0.64. When combined with other variables that affect winning percentage in close games, a linear regression on those explanatory variables has an adjusted R2 value of 0.79. This analysis shows that the ETM difference has a significant effect on winning close games, despite having little reliance on player performance.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":"23 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JSA-180231","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70124454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work describes a collaboration with a single collegiate volleyball team to leverage existing data to examine the potential of the plus/minus metric for player evaluation. Historically, volleyball players have been evaluated through a series of single skill metrics (e.g., number of aces per set and hitting percentage). The advantages of the plus/minus lie in the limited amount of information needed for its calculation (e.g., court presence and scoring) combined with its ability to fuse together both measured and unmeasured contributions. Unfortunately, the primary collection tool (Statcrew) for National Collegiate Athletic Association (NCAA) Women’s Volleyball, does not record the movement of the Libero, resulting in incomplete court presence information for a large percentage of plays. This paper introduces methodology to recover court presence information from standard play-by-play data. The recovery is in the form of a posterior distribution of player presence, which can then be used to not only calculate the plus/minus metric but also quantify the uncertainty of the metric due to the incomplete information. Although the presented methods and results were derived from a collaboration with a single team, the data source and methodology can be extended to multiple teams.
{"title":"Exploring the potential of the plus/minus in NCAA women’s volleyball via the recovery of court presence information","authors":"Zachary Hass, B. Craig","doi":"10.3233/JSA-180217","DOIUrl":"https://doi.org/10.3233/JSA-180217","url":null,"abstract":"This work describes a collaboration with a single collegiate volleyball team to leverage existing data to examine the potential of the plus/minus metric for player evaluation. Historically, volleyball players have been evaluated through a series of single skill metrics (e.g., number of aces per set and hitting percentage). The advantages of the plus/minus lie in the limited amount of information needed for its calculation (e.g., court presence and scoring) combined with its ability to fuse together both measured and unmeasured contributions. Unfortunately, the primary collection tool (Statcrew) for National Collegiate Athletic Association (NCAA) Women’s Volleyball, does not record the movement of the Libero, resulting in incomplete court presence information for a large percentage of plays. This paper introduces methodology to recover court presence information from standard play-by-play data. The recovery is in the form of a posterior distribution of player presence, which can then be used to not only calculate the plus/minus metric but also quantify the uncertainty of the metric due to the incomplete information. Although the presented methods and results were derived from a collaboration with a single team, the data source and methodology can be extended to multiple teams.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2018-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JSA-180217","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49516908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In amateur golf, lower handicap players “give strokes” to higher handicap players based on their handicap differential to make head-to-head matches fairer. In match play, the standard way to allocate handicap strokes uses the “course-defined hole ranking”. Using a bootstrapped simulation of over 70,000 matches based on 392 rounds of golf, we first show that the standard stroke allocation method and course-defined hole ranking favor the better player in 53% of matches. Then, we investigate the impact of three potential changes to stroke allocation: modifying the hole ranking; giving both players their full handicaps instead of using handicap differential; awarding extra strokes to the weaker player. Our two primary findings are: 1) fair matches can be achieved by giving the weaker player 0.5 extra strokes, which corresponds to a tie-breaker on a single hole; 2) giving both players their full handicap makes the fairness results robust to different hole rankings. Together, these simple changes can improve fairness in match play golf and improve generalizability to other courses.
{"title":"Improving fairness in match play golf through enhanced handicap allocation","authors":"T. Chan, David Madras, M. Puterman","doi":"10.3233/JSA-180184","DOIUrl":"https://doi.org/10.3233/JSA-180184","url":null,"abstract":"In amateur golf, lower handicap players “give strokes” to higher handicap players based on their handicap differential to make head-to-head matches fairer. In match play, the standard way to allocate handicap strokes uses the “course-defined hole ranking”. Using a bootstrapped simulation of over 70,000 matches based on 392 rounds of golf, we first show that the standard stroke allocation method and course-defined hole ranking favor the better player in 53% of matches. Then, we investigate the impact of three potential changes to stroke allocation: modifying the hole ranking; giving both players their full handicaps instead of using handicap differential; awarding extra strokes to the weaker player. Our two primary findings are: 1) fair matches can be achieved by giving the weaker player 0.5 extra strokes, which corresponds to a tie-breaker on a single hole; 2) giving both players their full handicap makes the fairness results robust to different hole rankings. Together, these simple changes can improve fairness in match play golf and improve generalizability to other courses.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2018-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JSA-180184","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47940601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Jayanth, Akas Anthony, G. Abhilasha, Noorni Shaik, G. Srinivasa
. Predicting the outcome of a game using players strength and weakness against the players of the opponent team by considering the statistics of a set of matches played by players helps captain and coaches to select the team and order the players. In this paper, we propose a supervised learning method using SVM model with linear, and nonlinear poly and RBF kernals to predict the outcome of the game against particular side by grouping the players at different levels in the order of play for both the teams. The comparison among different groups of players at same level gives the order of groups which contributes to winning probability. we also propose to develop a system which recommends a player for a specific role in a team by considering the past performances. we achieve this by finding the similar players by clustering all the players using k-means clustering and finding the five nearest players using k nearest neighbor (KNN) classifier. We calculate the ranking index for players using the game and players statistics extracted from a particular tournament. Experimental results demonstrate that, the n-dimensional data considered for modeling is not linearly separable. Hence the nonlinear SVM with RBF kernel outperforms from the linear and poly kernel. SVM with RFB kernel yields the accuracy of 75, precision of 83.5 and recall rate of 62.5. So we recommend the use of SVM with RBF kernel for game outcome prediction.
{"title":"A team recommendation system and outcome prediction for the game of cricket","authors":"S. Jayanth, Akas Anthony, G. Abhilasha, Noorni Shaik, G. Srinivasa","doi":"10.3233/JSA-170196","DOIUrl":"https://doi.org/10.3233/JSA-170196","url":null,"abstract":". Predicting the outcome of a game using players strength and weakness against the players of the opponent team by considering the statistics of a set of matches played by players helps captain and coaches to select the team and order the players. In this paper, we propose a supervised learning method using SVM model with linear, and nonlinear poly and RBF kernals to predict the outcome of the game against particular side by grouping the players at different levels in the order of play for both the teams. The comparison among different groups of players at same level gives the order of groups which contributes to winning probability. we also propose to develop a system which recommends a player for a specific role in a team by considering the past performances. we achieve this by finding the similar players by clustering all the players using k-means clustering and finding the five nearest players using k nearest neighbor (KNN) classifier. We calculate the ranking index for players using the game and players statistics extracted from a particular tournament. Experimental results demonstrate that, the n-dimensional data considered for modeling is not linearly separable. Hence the nonlinear SVM with RBF kernel outperforms from the linear and poly kernel. SVM with RFB kernel yields the accuracy of 75, precision of 83.5 and recall rate of 62.5. So we recommend the use of SVM with RBF kernel for game outcome prediction.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2018-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JSA-170196","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43501872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The purpose of this study was to assess side-out success and ways that points are obtained in relation to the result of the game in women’s college volleyball. A total of 2,435 rallies from 48 sets of the Missouri Valley Conference (NCAA Division I) were analyzed. The variables studied were: game phase, phase efficacy, reason for the success or error, number of times that the ball went over the net, result of the game, attack tempo, reception efficacy, and type of set. The findings provide reference values to guide the analysis of the volleyball team and understand the way winning teams score and build their side-out in the women’s college population. The results show the importance of side-out phase efficacy as a variable to monitor team performance in competition and the individual actions that correlate most with side-out phase success. The action that best differentiates winning and losing teams was the attack after reception. The results show the contribution of different ways to build the side-out. The actions with greater contribution were the ones that increased the setter’s possibilities to build the offense and accelerate the game, such as through the reception efficacy and the use of the jump set.
{"title":"Side-out success and ways that points are obtained in women’s college volleyball","authors":"J. Palao","doi":"10.3233/JSA-180153","DOIUrl":"https://doi.org/10.3233/JSA-180153","url":null,"abstract":"The purpose of this study was to assess side-out success and ways that points are obtained in relation to the result of the game in women’s college volleyball. A total of 2,435 rallies from 48 sets of the Missouri Valley Conference (NCAA Division I) were analyzed. The variables studied were: game phase, phase efficacy, reason for the success or error, number of times that the ball went over the net, result of the game, attack tempo, reception efficacy, and type of set. The findings provide reference values to guide the analysis of the volleyball team and understand the way winning teams score and build their side-out in the women’s college population. The results show the importance of side-out phase efficacy as a variable to monitor team performance in competition and the individual actions that correlate most with side-out phase success. The action that best differentiates winning and losing teams was the attack after reception. The results show the contribution of different ways to build the side-out. The actions with greater contribution were the ones that increased the setter’s possibilities to build the offense and accelerate the game, such as through the reception efficacy and the use of the jump set.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2018-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JSA-180153","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44743374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. D. Pasteur, E. Howerton, Preston Pozderac, S. Young, Jonathan Moore
{"title":"A flight-based metric for evaluating NFL punters","authors":"R. D. Pasteur, E. Howerton, Preston Pozderac, S. Young, Jonathan Moore","doi":"10.3233/JSA-180164","DOIUrl":"https://doi.org/10.3233/JSA-180164","url":null,"abstract":"","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2018-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JSA-180164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45516533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Television is becoming an increasingly critical revenue stream in the sports industry, as media rights deals in the four major North American sports (NBA, MLB, NHL, and NFL) continue to escalate by huge rates every time they are up for renewal. Live sports certainly captivate large numbers of viewers, and according to conventional wisdom, when two live events air at the same time, they compete head-to-head for those eyeballs. It is often discussed how NFL games allegedly crush ratings for the World Series or NBA playoff games devour the audience for their NHL playoff counterparts. If ratings are so critical to franchise bottom lines and competition does in fact hurt ratings, though, why then do so many sports willingly overlap while other parts of the year are left empty, flush with fans hungering for sports programming? This overlap can easily be prevented, as there is a vast stretch of the summer from mid-June to the end of August during which the only major sport in season is regular season MLB games—among the lowest rated programming of the four major sports. Would NHL or NBA playoff TV ratings increase if either pushed its calendar back and avoided directly competing with the other from April-June? Would World Series ratings cease their downward spiral if they moved up from October before getting caught in the supposed NFL black hole? In an industry where teams hire armies of statisticians, coaches, trainers, and scouts to claw at every last inch of competitive edge and where leagues squeeze out every last drop of revenue, one would think someone would notice if that were the case—or does programming competition from other sports simply have little effect on ratings? This paper attempts to isolate the effects of overlap from each sport, examine how that competition hurts viewership in each league, and quantify the value lost due to that overlap. We find that competition can have very damaging effects for TV viewership for every sport, most notably the NHL, and these losses can significantly diminish the value of a network’s investments in sports
{"title":"Competition between sports hurts TV ratings: How to shift league calendars to optimize viewership","authors":"Jim Pagels","doi":"10.3233/JSA-170117","DOIUrl":"https://doi.org/10.3233/JSA-170117","url":null,"abstract":"Television is becoming an increasingly critical revenue stream in the sports industry, as media rights deals in the four major North American sports (NBA, MLB, NHL, and NFL) continue to escalate by huge rates every time they are up for renewal. Live sports certainly captivate large numbers of viewers, and according to conventional wisdom, when two live events air at the same time, they compete head-to-head for those eyeballs. It is often discussed how NFL games allegedly crush ratings for the World Series or NBA playoff games devour the audience for their NHL playoff counterparts. If ratings are so critical to franchise bottom lines and competition does in fact hurt ratings, though, why then do so many sports willingly overlap while other parts of the year are left empty, flush with fans hungering for sports programming? This overlap can easily be prevented, as there is a vast stretch of the summer from mid-June to the end of August during which the only major sport in season is regular season MLB games—among the lowest rated programming of the four major sports. Would NHL or NBA playoff TV ratings increase if either pushed its calendar back and avoided directly competing with the other from April-June? Would World Series ratings cease their downward spiral if they moved up from October before getting caught in the supposed NFL black hole? In an industry where teams hire armies of statisticians, coaches, trainers, and scouts to claw at every last inch of competitive edge and where leagues squeeze out every last drop of revenue, one would think someone would notice if that were the case—or does programming competition from other sports simply have little effect on ratings? This paper attempts to isolate the effects of overlap from each sport, examine how that competition hurts viewership in each league, and quantify the value lost due to that overlap. We find that competition can have very damaging effects for TV viewership for every sport, most notably the NHL, and these losses can significantly diminish the value of a network’s investments in sports","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2018-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JSA-170117","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42229430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A new figure skating competition was introduced at the 2014 Winter Olympic Games (WOG) – the team event. The introduction of this new competition raises questions of what would have happened if a team event had been contested in past WOG. This paper develops a method for determining which teams might have earned medals if the team event had been held in the past, and applies the method to a hypothetical competition for 2010. This paper also identifies relative contributions of skaters to their countries’ teams in the hypothetical competition. These methods can be useful for fans and for electors who vote on candidates for figure skating halls of fame.
{"title":"What if a figure skating team event had been held at past Winter Olympic Games? An analysis of a hypothetical competition","authors":"Diana S. Cheng, P. Coughlin","doi":"10.3233/JSA-170148","DOIUrl":"https://doi.org/10.3233/JSA-170148","url":null,"abstract":"A new figure skating competition was introduced at the 2014 Winter Olympic Games (WOG) – the team event. The introduction of this new competition raises questions of what would have happened if a team event had been contested in past WOG. This paper develops a method for determining which teams might have earned medals if the team event had been held in the past, and applies the method to a hypothetical competition for 2010. This paper also identifies relative contributions of skaters to their countries’ teams in the hypothetical competition. These methods can be useful for fans and for electors who vote on candidates for figure skating halls of fame.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2018-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JSA-170148","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43254088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The paper utilizes the entire cricketing data between England and Australia – Test and one-day international (ODI) matches played between 1877-2015 and 1971-2015, respectively – to provide an econometric perspective on the EnglandAustralia rivalry. We employ the production function approach of Schofield (1988) and model Test match outcomes (loss, draw or win) using an ordinal probit model and ODI outcomes (loss or win) using a binary probit model. The results show that input measures critical to winning are different for the two formats and consequently a team should adopt different strategies in Test and ODI matches. We further show that influences which are perceived as important to match outcomes, including electing to bat first after winning the toss and effect of weather conditions, do not have any statistical support. However, there is strong evidence that England is at a disadvantage while playing a Test match in Australia. Besides, we find that home bias as typically defined in the literature may not necessarily indicate favoritism by umpires. The estimated models fit well and correctly predict about 70% of Test match outcomes and 95% of ODI outcomes.
{"title":"Are strategies for success different in test cricket and one-day internationals? Evidence from England-Australia rivalry1","authors":"Nafisa Lohawala, M. A. Rahman","doi":"10.3233/JSA-180191","DOIUrl":"https://doi.org/10.3233/JSA-180191","url":null,"abstract":"The paper utilizes the entire cricketing data between England and Australia – Test and one-day international (ODI) matches played between 1877-2015 and 1971-2015, respectively – to provide an econometric perspective on the EnglandAustralia rivalry. We employ the production function approach of Schofield (1988) and model Test match outcomes (loss, draw or win) using an ordinal probit model and ODI outcomes (loss or win) using a binary probit model. The results show that input measures critical to winning are different for the two formats and consequently a team should adopt different strategies in Test and ODI matches. We further show that influences which are perceived as important to match outcomes, including electing to bat first after winning the toss and effect of weather conditions, do not have any statistical support. However, there is strong evidence that England is at a disadvantage while playing a Test match in Australia. Besides, we find that home bias as typically defined in the literature may not necessarily indicate favoritism by umpires. The estimated models fit well and correctly predict about 70% of Test match outcomes and 95% of ODI outcomes.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2018-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JSA-180191","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44045984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Every year millions of people participate in big-city marathons around the world, with such events routinely attracting thousands and even tens of thousands of participants. Careful pacing is widely considered to be an important determinant of success in the marathon and, come race-day, most participants will have decided on a pacing strategy to ensure they manage their energy levels and optimise their finish-times. While researchers have examined the pacing of elite athletes, recreational runners are less well understood. We present an analysis of 1.7 million recreational runners, focusing on pacing at the start and end of the marathon, two particularly important race stages. We show how starting or finishing too quickly can result in poorer finish-times, because fast starts tend to be very fast, leading to endurance problems later, while fast finishes suggest overly cautious pacing earlier in the race. We find that women tend to pace their race more effectively than men, but they can be more cautious too, costing them minutes overall. These findings help to quantify the costs of uneven pacing at the start and end of a marathon, and may help to improve coaching and performance in endurance races.
{"title":"Fast starters and slow finishers: A large-scale data analysis of pacing at the beginning and end of the marathon for recreational runners","authors":"Barry Smyth","doi":"10.3233/JSA-170205","DOIUrl":"https://doi.org/10.3233/JSA-170205","url":null,"abstract":"Every year millions of people participate in big-city marathons around the world, with such events routinely attracting thousands and even tens of thousands of participants. Careful pacing is widely considered to be an important determinant of success in the marathon and, come race-day, most participants will have decided on a pacing strategy to ensure they manage their energy levels and optimise their finish-times. While researchers have examined the pacing of elite athletes, recreational runners are less well understood. We present an analysis of 1.7 million recreational runners, focusing on pacing at the start and end of the marathon, two particularly important race stages. We show how starting or finishing too quickly can result in poorer finish-times, because fast starts tend to be very fast, leading to endurance problems later, while fast finishes suggest overly cautious pacing earlier in the race. We find that women tend to pace their race more effectively than men, but they can be more cautious too, costing them minutes overall. These findings help to quantify the costs of uneven pacing at the start and end of a marathon, and may help to improve coaching and performance in endurance races.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2018-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JSA-170205","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42873293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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