Journal of Sports Analytics - Volume 6, issue 1

Authors: Rotthoff, Kurt W.

Article Type: Research Article

Abstract: Difficulty bias was found in Morgan and Rotthoff (2014) , claiming that trying something harder leads to a higher overall ranking, ceteris paribus . This was measured in elite level gymnastics, which at some meets allows for a unique measurement of this form of bias. Multiple studies also find evidence of an overall order bias. I analyze if these two forms of bias still exist in elite level gymnastics. I continue to find evidence of difficulty bias with this updated dataset and find strong evidence that these biases are different across genders – females have evidence of a large and …significant difficulty bias, whereas males do not. However, the evidence of an overall order bias does not exist in this dataset. Show more

Keywords: Difficulty bias, Judging bias, Reference bias JEL: L10, L83, D81, J70, Z2, L10, L83, D81, J70, Z2

DOI: 10.3233/JSA-200272

Citation: Journal of Sports Analytics, vol. 6, no. 1, pp. 1-11, 2020

Authors: Waquil, Alice Paul | Horta, Eduardo | Moraes, Jean Carlo

Article Type: Research Article

Abstract: In football, many people argue that in knock-out competitions the team that plays the second game in its stadium would have an advantage, a greater probability of victory in the final outcome of a two leg knock-out match. The purpose of this study is to verify the veracity of this statement using data from the Brazil Cup. We find evidence that the ability spread between the teams participating in a match is the main factor that explains the qualification of one of the teams for the next round. Until 2018, there were three criteria for break the tie in the …Brazil Cup, they were used in the respectively order: goal difference, away goals rule, penalty shootout (there is no extra-time in any play-off of the championship). It is estimated that 36% of the matches end tied and need a criterion to determine the winner. Of these, 51% use the goal difference decision, 29% use the away goals rule and 20% penalty shoot-out. When considering the championship in general there is evidence that the home team wins the match in approximately 63% of the matches, a significant advantage. However, in the confrontations that were decided by the away goals rule or the penalty shoot-out, the home team wins percentage is 20% lower, indicating that these criteria level the odds of both teams. Show more

Keywords: Home advantage, football, Brazil cup, away goals rule

DOI: 10.3233/JSA-200370

Citation: Journal of Sports Analytics, vol. 6, no. 1, pp. 13-24, 2020

Authors: South, Charles | Egros, Edward

Article Type: Research Article

Abstract: There are many reasons why data scientists and fans of college football would want to forecast the outcome of games – gambling, game preparation and academic research, for example. As advanced statistical methods become more readily accessible, so do the opportunities to develop robust forecasting models. Using data from the 2011 to 2014 seasons, we implemented a variety of advanced modeling techniques to determine which best forecasts the outcome of games. These methods included ridge regression, the lasso, the elastic net, neural networks, random forests, k-nearest neighbors, stochastic gradient boosting, and a Bayesian regression model. To evaluate the efficacy of …the proposed models, we tested them on data from the 2015 season. The top performers – lasso regression, a Bayesian regression with team-specific variances, stochastic gradient boosting, and random forests – predicted the correct outcome over 70% of the time, and the lasso model proved most accurate at predicting win-loss outcomes in the 2015 test data set. Show more

Keywords: Keywords: NCAA football, prediction, machine learning, Bayesian, variable selection

DOI: 10.3233/JSA-190314

Citation: Journal of Sports Analytics, vol. 6, no. 1, pp. 25-33, 2020

Authors: Joash Fernandes, Craig | Yakubov, Ronen | Li, Yuze | Prasad, Amrit Kumar | Chan, Timothy C.Y.

Article Type: Research Article

Abstract: This paper aims to develop an interpretable machine learning model to predict plays (pass versus rush) in the National Football League that will be useful for players and coaches in real time. Using data from the 2013–2014 to 2016–2017 NFL regular seasons, which included 1034 games and 130,344 pass/rush plays, we first develop and compare several machine learning models to determine the maximum possible prediction accuracy. The best performing model, a neural network, achieves a prediction accuracy of 75.3%, which is competitive with the state-of-the-art methods applied to other datasets. Then, we search over a family of simple decision tree …models to identify one that captures 86% of the prediction accuracy of the neural network yet can be easily memorized and implemented in an actual game. We extend the analysis to building decision tree models tailored for each of the 32 NFL teams, obtaining accuracies ranging from 64.7% to 82.5%. Overall, our decision tree models can be a useful tool for coaches and players to improve their chances of stopping an offensive play. Show more

Keywords: Machine learning, neural networks, decision trees, play prediction, NFL

DOI: 10.3233/JSA-190348

Citation: Journal of Sports Analytics, vol. 6, no. 1, pp. 35-43, 2020

Authors: David Pifer, N. | Huml, Matt R.

Article Type: Research Article

Abstract: In recent years, it has become common for media members and other college football affiliates to associate a program’s turnaround with the Year-Two Effect, a phenomenon whereby an NCAA Division I football program is expected to make large improvements during a head coach’s second season in charge. However, like many of the mass media’s sport truisms, this phenomenon has gone untested and unexplored in the broader realm of empirical literature. Given the big business that is modern day college football, where revenues have reached the billions of dollars, and tens of millions are being spent on coaching salaries, bonuses, contract …extensions, and buyouts, further examinations into the Year-Two Effect, its causes, and its implications are warranted from both the analytical and economical perspectives. Using two-way fixed effects panel regression models to analyze 11 seasons (2007-17) of data for 114 NCAA Division I Football Bowl Subdivision (FBS) programs, this study found support for the Year-Two Effect’s existence, particularly in situations where coaches were replacing a prior coach that had been fired for on-field performance reasons. In addition, teams also tended to significantly improve their recruiting rankings and commit fewer turnovers during a head coach’s second season at the helm. Show more

Keywords: NCAA football, coaching tenure, second season, improvement, analytics

DOI: 10.3233/JSA-190362

Citation: Journal of Sports Analytics, vol. 6, no. 1, pp. 45-60, 2020

Authors: Pradhan, Sean | Abdourazakou, Yann

Article Type: Research Article

Abstract: The utilisation of performance criteria to determine power rankings of various teams is useful for a vast number of audiences in many traditional sports, such as soccer, football, baseball, basketball, hockey, and the like. With market hyper-competition and viewership growth in the digital sector of eSports and related professional video gaming, the usage of performance evaluation in the scholarly literature is relatively scant. Thus, the current study provides a performance assessment and ranking structure in eSports. Applying a hybrid technique developed by previous research involving a series of Multi-Criteria Decision-Making (MCDM) tools, those being: Grey Relational Analysis (GRA), Simple …Additive Weighting (SAW), and the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), this study develops power rankings for professional teams in the popular multiplayer online battle arena (MOBA) game, Defense of the Ancients (Dota ) 2 . Further analyses offer a comparison of the produced ranking structure to other metrics commonly used in eSports evaluation, such as Elo and Glicko ratings. The findings of this study offer implications for players, teams, performance analysts, oddsmakers, consumers, among others. Recommendations and future directions of this research are also provided. Show more

Keywords: Ranking, eSports, multi-criteria decision-making (MCDM), grey relational analysis (GRA), simple additive weighting (SAW), technique for order preference by similarity to the ideal solution (TOPSIS)

DOI: 10.3233/JSA-190420

Citation: Journal of Sports Analytics, vol. 6, no. 1, pp. 61-73, 2020