COVID-19 countermeasures, Major League Baseball, and the home field advantage: Simulating the 2020 season using logit regression and a neural network

Introduction

The 2019–2020 pandemic from the novel coronavirus (COVID-19) has brought unprecedented countermeasures to every sector of the economy, including individuals, groups, institutions, and industries. The sports industry took one of the biggest hits, with all major leagues in the U.S. cancelling or halting their events. While these actions were necessary to address the public health concern, each segment is now floating various proposals to resume operations and give some relief to the significant portion of the economy that the sports industry comprises.

Major League Baseball (MLB) is likely to be the first American professional sporting league to resume, probably in May or June (Passan, 2020). Players are willing (although not all players agree to the method), the League is willing, the Arizona government is willing, and health professionals have approved a plan to move forward, known as the Arizona Plan. This plan calls for players, coaches, and staff to be quarantined in hotels around the Phoenix area, and to play in empty ballparks that include the ten Cactus League Spring Training parks, Chase Field, and other Phoenix ballparks. One interesting aspect of this arrangement is that the stadium size will not matter because there will not be any fans. This will be an opportunity for MLB to get back into the spotlight and accumulate massive television viewership that MLB has not seen in decades. The experience will be completely optimized for TV viewing, and so the league will finally be able to experiment with proposed rule changes, including removing mound visits to make the game go faster, adding a Robo Umpire, which has already been successfully tested last season via a partnership with the independent Atlantic League (Bogage, 2019), and an expanded roster giving players more rest due to the extremely hot temperatures of Phoenix. While all of this will alter predictions on who's going to the playoffs, probably the biggest impact that this plan will have on the games is the lack of the home field advantage (HFA): the advantage that the home team has over the visiting team due to the home team having fans, the familiarity of the home team to their own ball park, and the away team having to travel.

Baseball has been shown in previous studies to be less susceptible to the HFA effect than other professional sports (Edwards & Archambault, 1979; Gómez et al., 2011; Pollard et al., 2017). Despite this, there is a measurable home field advantage in baseball, as shown by Jones (2015); Jones (2018)). Building on this, we extend the analysis for the MLB under uncertainty of which scenario the League will be following for the 2020 season. In particular, we simulate the win-loss probabilities for three different scenarios as well as estimate the home advantage for each team using the past three seasons’ data. This estimation helps us understand how individual team performance is going to be impacted as they prepare for the 2020 season in the new circumstances.

Methods

Results

The summary statistics of the training data is contained in Table 1. The logit results from Equation 1, without the fixed effects, are reported in Table 2. Both log odds ratios and the MEs are reported in this table. The results show that the individual regressors included in the model show plausible impacts. Looking at the log-odds ratios, home games and the home team’s previous win-loss percentage (WL%) are more likely to result in a win but the opponent’s WL% is less likely to result a loss for the home team. These results support the presence of the HFA. The right half of the table shows the MEs for each variable. We are mainly interested in the MEe for the Home variable, which is 0.064. This means, the marginal probability of winning a game at home versus away field goes up by 6.4%. This is the average HFA for all of the teams as a whole. The HFA for each team is presented in Figure 1. In our sample, PHI seems to have the highest home advantage and HOU seems to have the lowest (negative, in fact) home advantage.

Figure 1. MLB home field advantage effect of individual teams.

The model was trained using the 2017–2019 MLB regular season games. The schedule for the 2020 season was estimated using the schedule from the 2019 season. While the dates will be off slightly, the team pairings will be nearly the same. The wins and losses of the 100 simulations were added to form the result of the 2020 season. The overall results are visualized in Figure 2, while the divisional results shown in Table 3. Table 4 provides statistics calculated during each season and averaged. This includes the correlation between the full season and the half and no-fan seasons using both the overall rankings and the win-loss percent (WL%). The full seasons rank correlations are higher with the no-fans seasons (0.825) than the half seasons (0.735). The correlations using WL% is similar. The standard deviation of the predicted win probabilities is lower for the no-fans seasons (0.073) than the full (0.085) and half seasons (0.085). The home effect was correlated with the win probabilities’ standard deviations and is negative for the no fans seasons (-0.221). In other words, the higher the home effect, the lower the variance.

Figure 2. MLB season 2020 change in simulated rank after 100 simulations.

This neural network was also used as the win predictor in 100 simulations and the results are very similar to the logit win prediction model, which shows robustness in the simulation results. Table 4 shows the statistical results of both models. The correlation and standard deviation differences are approximately the same between the two models.

The results of the simulations are available as Extended data (Ehrlich, 2020b) and the code necessary for replicating the results, including training the models, are hosted on GitHub (Ehrlich, 2020a).

Discussion

Based on the above results, since the team-home effect is symmetric between home and away, teams will not necessarily win or lose any additional games in neutral stadiums as teams with a high home field effect will lose more neutral games that would have been at home but will win more neutral games that would have been away. The greater the home-team ME, the less variance there will in of the predicted win probabilities. To verify this assumption, we calculated the correlation of HomeEffects and the standard deviation (SD) of win probabilities between a full (0.361) and no-fan season (-0.221). Since the home effect is symmetric for each team (the away field disadvantage = -the home field advantage), decreasing the variance does not affect the overall expected WL% for each team. However, the result of individual games will be different since home effect is asymmetric between teams. For example, if the Cubs (highest home effect in the NL Central) plays the Cardinals (lowest home effect in the NL Central), the Cubs will have a larger advantage playing at home then the Cardinals will have playing at home (besides team fixed effects). These differences are removed with the No-Fan scenario and the outcome will be solely based upon the talent of the teams. However, on average there only a slight change of overall WL% (or playoff berth), just the SD of the results (see Table 3). Without fans, any advantage (or disadvantage) from home field advantage, which cause higher levels of variance, is removed. This stabilizes the outcome based upon true team talent. As fewer games have been played, the half-season will have more upsets, but the SD is close to the same as the full season.

Conclusion

This paper analyzes the previous season MLB data to estimate the win-loss probabilities for the 2020 season for each of the 30 teams in the League using logit regressions and a neural network. The Arizona Plan’s neutralization of HFA would not significantly affect the overall outcome of the season. In fact, our model predicts that the Arizona Plan season will produce season results that are based more on the true talent of the teams. Further, our simulation demonstrates that there will be less variance in the win probability between any two teams, which we estimate will cause a larger divide between the best and worst teams. In conclusion, we believe that the results of the Arizona Plan will be similar to a regular season with fans, and that the teams’ standings at the end of the regular season will be more predictable than a normal season.

Data availability