Journal of Sports Analytics

This article examines the use of the PageRank algorithm to rank the teams and predict team performance in the tournament. This method has the potential to be utilized as an alternative method to choose tournament participants, as opposed to the traditional ranking and seeding process currently employed by the NCAA. PageRank allows for the consideration of all games played during the regular season (average of 5832 games per season) and for customizable performance weights in the prediction. The PageRank algorithm is a viable tool in predicting tournament outcomes due to depth and extensiveness of the data. The PageRank analysis helped to predict over half of the tournament game outcomes correctly in 2014-2018 and produced an average bracket score of 81.8 points out of 192 possible over the same 5 years.

Data from the pool rounds of three HSBC World Rugby Sevens competitions (2016–17, 2017–18, and 2018–19) are used to investigate the number of tries required to win in international rugby sevens. The data consist of 4,391 tries scored in 720 matches (1,440 team performances) and are used to calculate the probability of winning a match given that T tries are scored (P[W|T]). The distribution of the number of tries scored by each team ranges from zero to nine and is shown to be well-represented by a Poisson distribution computed from the mean value of tries scored in that competition. The number of tries scored by the winning team in each match within a competition is well-described by a Gamma function evaluated at the integer number of tries scored with parameters derived from the data set. This appears to be a novel result not previously reported in the literature. Generalizing within each competition, teams scoring either zero tries or one try have less than a 2% chance of winning; those scoring two tries win 10% to 20% of the time; three tries result in nearly a 50% chance of winning; teams scoring four tries are almost sure to win (around 90%); and that for teams scoring five or more tries winning is virtually assured. Based upon the results from these three tournaments we conclude that competitive teams should strive to score three or more tries per match and that there is no winning advantage accrued by scoring more than five tries.

Box score statistics are the baseline measures of performance for National Collegiate Athletic Association (NCAA) basketball. Between the 2011-2012 and 2015-2016 seasons, NCAA teams performed better at home compared to on the road in nearly all box score statistics across both genders and all three divisions. Using box score data from over 100,000 games spanning the three divisions for both women and men, we examine the factors underlying this discrepancy. The prevalence of neutral location games in the NCAA provides an additional angle through which to examine the gaps in box score statistic performance, which we believe has been underutilized in existing literature. We also estimate a regression model to quantify the home court advantages for box score statistics after controlling for other factors such as number of possessions, and team strength. Additionally, we examine the biases of scorekeepers and referees. We present evidence that scorekeepers tend to have greater home team biases when observing men compared to women, higher divisions compared to lower divisions, and stronger teams compared to weaker teams. Finally, we present statistically significant results indicating referee decisions are impacted by attendance, with larger crowds resulting in greater bias in favor of the home team.

We introduce the Tournament Rank Probability Score (TRPS) as a measure to evaluate and compare pre-tournament predictions, where predictions of the full tournament results are required to be available before the tournament begins. The TRPS handles partial ranking of teams, gives credit to predictions that are only slightly wrong, and can be modified with weights to stress the importance of particular features of the tournament prediction. Thus, the Tournament Rank Prediction Score is more flexible than the commonly preferred log loss score for such tasks. In addition, we show how predictions from historic tournaments can be optimally combined into ensemble predictions in order to maximize the TRPS for a new tournament.

The 2018-2019 NCAA men’s basketball tournament featured 32 automatic qualifiers and 36 at-large selections. A new metric, College Basketball Rating (CBR), agrees with 30 of the 36 at-large selections but disagrees with the other six teams. CBR finds St. John’s, Temple, Seton Hall, Ole Miss, Baylor, and Minnesota unworthy of an at-large selection and instead prefers Clemson, Texas, Lipscomb, Nebraska, NC State, and TCU. In the most extreme case, CBR identifies 45 non-tournament teams more deserving of an at-large selection than St. John’s. This paper highlights the numerous benefits of CBR and presents strong evidence in favor of its use in determining future NCAA tournament at-large selections.