Beyond the Ninth: Exploring Alternative Extra Inning Rules in College and MLB Baseball

In the COVID-19-shortened Major League Baseball (MLB) season of 2020, an extra innings rule was introduced to shorten game length. The goals were to reduce pitcher usage and minimize player exposure during the pandemic. Under this rule, the player who precedes the first batter in the inning in the batting order starts on second base to begin extra innings. Commonly referred to as the “ghost runner” rule, because the runner did not earn their position via a plate appearance, the rule has remained in place since 2021. While Major League Baseball has already addressed extra-inning length through rule changes, college baseball continues to play under traditional extra-inning rules despite a higher run-scoring environment, and greater academic and travel constraints.

The impact of the rule is clear when comparing data. In 2019, the final season under regular extra innings rules, MLB teams played 270 innings beyond the 10th inning, including four games that lasted 18 or more innings, which is over twice the length of a standard game. By contrast, in 2021, with the ghost runner rule in effect, only 84 extra innings were played, with just three games extending beyond the 12th inning. This represents a 69% reduction in innings played beyond the 10th.1

The rule functions by increasing the likelihood of run-scoring disparity, which is necessary to end the game. A half-inning refers to either the visiting team’s at-bats or the home team’s at-bats within a single inning. Under typical Major League Baseball run-scoring conditions using 2023 data, approximately 72% of half-innings that begin with no runners on base result in zero runs scored. If both teams fail to score, the game continues. By starting extra innings with a runner on second base, the probability of a scoreless half-inning falls to roughly 40%. This increase in the likelihood of scoring makes it more probable that the two teams will score different numbers of runs, thereby bringing the game to a conclusion more quickly.

Economically and competitively, shortening games offers numerous benefits. From an economic perspective, shorter games reduce costs associated with extended work hours for stadium staff, and security personnel, while improving job satisfaction. Operational expenses, such as lighting, utilities, and concessions, can also be reduced. Broadcast networks benefit from greater predictability in game length, allowing for more accurate scheduling and advertisement planning, ultimately boosting revenue. Fans are also more likely to stay until the end of a game if they know it will end soon, improving their experience and increasing the likelihood of repeat attendance. This benefits both the team and venue economically.

Players also gain critical time for rest, recovery, and travel, which helps maintain peak performance over a long season. Competitively, shorter games reduce strain on pitching staffs, ensuring more availability for upcoming games and decreasing the risk of injuries caused by overuse.

Shorter games would also grant some relief to college players. The NCAA has no ghost runner rule, though two teams or conferences can mutually agree to play with that rule in effect. Conversations with college coaches indicate that the rule is rarely agreed to with one coach indicating it occurs in fewer than 2% of games. The schedule is grueling, and players could benefit from additional time for academics, personal commitments, or recovery.

The purpose of this paper is to analyze the effect of alternative extra-inning rules on game length. In particular, we examine how Major League Baseball’s current extra-inning framework could be extended to bring games to a conclusion more quickly, and how adopting the MLB rule, given that college baseball currently has no modified extra-inning rule, would be expected to change game outcomes at the college level. We also evaluate how more aggressive alternatives, such as beginning extra innings with the bases loaded, would affect both Major League Baseball and college baseball, leagues that operate in different run-scoring environments. Because the rules governing extra innings already differ between MLB and college baseball, understanding the consequences of introducing and further extending such rules is essential. Central to this analysis is run-scoring disparity: if both teams score the same number of runs in an extra inning, the game continues, whereas any difference in runs scored ends the contest.

Run-scoring environments differ between MLB and college baseball. In 2023, MLB teams average 0.521 runs per half-inning, while college teams score an average of 0.791 runs per half-inning. College conferences may have an even greater incentive to shorten games due to travel constraints, as many college teams do not travel by air charter like MLB teams. Thus, college leagues might consider rules that end games even more efficiently.2 Because college baseball produces higher run-scoring per inning, our results show that identical extra-inning rules resolve college games even faster than MLB games, amplifying both the benefits and trade-offs of runner-on-base formats.

This paper does not argue that games should be shorter; rather, it evaluates how different extra innings rules impact game length. Our methodology predicts the length of extra-inning games without requiring leagues to experiment with different rules during actual games or seasons.

We find that starting extra innings with the bases loaded results in the fewest innings required to end a game in both MLB and college baseball, though leagues may reject this rule because it could lead to significantly higher scores or seem too extreme. For MLB, the current rule of starting with a runner on second has already reduced the likelihood of games extending beyond the 10th inning by almost half and makes it highly unlikely for games to go past the 13th inning.

In college baseball, all versions of starting with runners on base would reduce game length compared to current rules, in which no runners are put on base and the game continues until one team wins. Using MLB’s rule, in which a runner starts on second base, would end games even more quickly than MLB games end under the same rule. Our analysis predicts a 48% reduction in extra innings beyond the 10th, with less than 1% of games extending beyond the 12th inning.

The remainder of this paper discusses the data and methodology used, presents results for both MLB and college baseball, and concludes with a summary of findings.

DATA

For MLB data, we use play-by-play data from Retrosheet. Specifically, we analyze Retrosheet data from 2023, which include every MLB play during the season.3 For college data, we utilize datasets from 6–4–3 Charts, a platform that aggregates college baseball data from various sources and provides structured insights to subscribing teams. The 6–4–3 Charts data used in this study are limited to consolidated information regarding all of college baseball; no individual team data are included in the analysis.

METHODOLOGY

Pete Palmer and John Thorn (1984) are widely credited with introducing the 24 Base-Out Run Expectancy Matrix in baseball analytics, a framework further popularized by Tom Tango, Mitchell Lichtman, and Andre Dolphin (2007). David Hyland (2022) employs the 24 Base-Out Run Expectancy Matrix to investigate the impact of MLB’s extra-inning rules. Our methodology follows a similar approach.

The 24 Base-Out Run Expectancy Matrix quantifies the number of expected runs based on the base and out situation at the start of a play. For instance, in a typical half-inning beginning with no one on base and no outs, the expected runs are calculated as the average number of runs scored over the remainder of the inning. The run expectancy values for the other 23 base-out states are derived by averaging the runs scored from each respective state until the half-inning concludes. These expectancy values are empirical and can vary across leagues, levels of competition, and over time due to factors such as rule changes or trends in pitcher and batter performance.

Using 2023 MLB data, we find that teams score an average of 0.521 runs per half-inning. In contrast, college baseball teams score an average of 0.791 runs per half-inning. We analyze both MLB and college datasets to examine how different starting configurations for runners on base influence game length in extra innings.

A key assumption in our analysis is that scoring events for the two teams in an inning are independent. For our data, the correlation between scores for the visiting and home teams within the same inning is 0.016, supporting this assumption.

The following equation shows the probability of a game continuing after an extra inning:

For both MLB and college baseball, the probability of scoring 8 or more runs in an inning is negligible. Furthermore, scenarios where both teams score the same number of runs beyond 7 are exceedingly rare. Thus, for the purposes of this study, we set N=8.

RESULTS—MLB DATA

Table 1 displays the probability of scoring 0 to 8 runs based on the existing baserunner state. Notably, the first row provides the baseline probability for a traditional inning starting with no runners on base. Multiplying each probability in the first row by the corresponding number of runs yields the average number of runs scored in a typical inning. For instance, summing this value over nine innings results in an average of 4.5 runs scored per game per team.

The rows below the first show the conditional probabilities of scoring runs when starting with runners already on base. For example, the second row represents probabilities when starting with a runner on first base (“100”), while the third row represents starting with a runner on second base (“010”), which reflects the current MLB extra-inning rule.

These probabilities highlight the significant impact of starting base runner states on expected run outcomes. For instance, with no runners on base (“000”), the probability of scoring no runs is 72.2%.With a runner on second (“010”), this drops to 40.1%, reflecting a much higher likelihood of scoring.

Using probabilities from Table 1, Table 2 calculates the likelihood of both teams scoring the same number of runs based on starting base runner states.

The key findings from Table 2 is that starting with no runners on base (“000”) yields the highest probability (54.9%) of the game continuing into the next inning. Adding runners significantly reduces this probability. For example, starting with a runner on first (“100”) lowers the probability to 38.8%, while starting with bases loaded (“111”) drops it to just 17.1%.

This pattern underscores how beginning extra innings with runners on base accelerates game resolution. Notably, starting with a runner on third (“001”) results in a less steep drop compared to other scenarios, due to the fact that it is easier to score the runner from third and a likely outcome is both teams scoring 1 run.

Table 3 shows the percentage of games that would end in each inning under various starting base runner scenarios. Under traditional rules (“000”), only 45.1% of games conclude in the 10th inning, with nearly 10% lasting beyond the 13th inning. This allows for rare but memorable marathon games, with about 1% extending to 17 innings or more.

Starting with a runner on second (“010”) under current MLB rules results in 69.8% of games ending in the 10th inning and less than a 3% chance of games extending beyond 13 innings. Starting with bases loaded (“111”) ends 82.9% of games in the 10th inning, making it the fastest game-resolution scenario.

Another thing to consider besides how many extra innings a game will go is how many extra runs will be scored in total during the extra innings. If there are a lot of extra runs scored from “Ghost Runners” does it still feel like a game of baseball?

Table 4 calculates the expected total runs scored during extra innings, conditional on the starting base runner state. Under traditional rules (“000”), the expected runs scored is 1.14 per inning.4 Starting with a runner on second (“010”) increases expected runs to 1.59, representing an additional 0.35 runs per inning compared to traditional rules. Starting with bases loaded (“111”) leads to the highest expected runs, at 2.72 per inning which is a significant increase of 1.58 runs compared to traditional rules.

This increase in scoring reflects the strategic emphasis on resolving games more quickly but could alter the dynamics of managerial decision-making and fan experience.

 

Table 5. College Baseball Expected Probability of Scoring Runs for the Rest of the Half-Inning

RESULTS—COLLEGE DATA

Table 5 presents the probabilities of scoring 0 to 8 runs in an inning under different starting base runner scenarios for college baseball. Similar to the MLB data in Table 1, the first row represents the baseline probabilities when starting with no runners on base (“000”). However, college baseball teams are less likely to score 0 runs in an inning compared to MLB teams, with a 63.4% chance versus 72.2%. This reflects a higher scoring environment in college baseball, likely due to differences in talent levels, ballpark dimensions, and pitching depth.

For instance, starting with no runners on base (“000”) leads to an average of 0.79 runs scored per half-inning in college baseball, which is slightly higher than the MLB average of 0.521 runs. Adding a runner on second base (“010”), as used in the MLB extra-inning rule, increases the probability of scoring to 32.2% for one run and to 16.8% for two runs, further amplifying the scoring environment in college baseball.

Table 6 provides the probability of both teams scoring the same number of runs under various starting base runner conditions. In college baseball, the probability of both teams scoring 0 runs when starting with no runners on base (“000”) is 44.1%, 15% lower than the MLB equivalent of 54.9%. This suggests that college extra-inning games are inherently more likely to resolve without requiring adjustments to starting base runner conditions.

For example, starting with a runner on second base (“010”) lowers the probability of both teams scoring the same number of runs to 23.0%, compared to 28.8% in MLB. This reflects a faster resolution of games in college baseball due to a greater likelihood of scoring by at least one team in each inning.

Table 7 illustrates the percentage of games that would end in each inning under different starting base runner scenarios. Under traditional rules with no runners on base (“000”), 55.9% of college baseball games would end in the 10th inning, compared to 45.1% in MLB. Similarly, the percentage of games lasting beyond 13 innings is lower in college baseball (approximately 3%) than in MLB (nearly 10%).

 

Table 7. College Baseball Percent of Time Game Would Be Over in Each Inning if Extra Inning Base Runners Start on Bases

Table 8 calculates the expected total runs scored during extra innings based on starting base runner scenarios. Under traditional rules (“000”), college teams score an average of 0.79 runs per inning, compared to 1.14 in MLB. Starting with a runner on second base (“010”) increases expected runs to 1.58, aligning closely with the MLB’s expected value of 1.59.

The most dramatic increases occur when starting with bases loaded (“111”), where expected runs reach 3.03 in college baseball versus 2.72 in MLB. This emphasizes the higher scoring environment of college baseball and highlights the potential impact of these rule modifications on game outcomes and strategy.

SUMMARY

For both MLB and college baseball, starting extra innings with bases loaded resolves games the fastest, reducing extra innings beyond the 10th by 97% in MLB and 95% in college baseball. However, this comes at the cost of significantly increased scoring, adding an additional 2.72 to 3.03 runs per game compared to traditional rules.

Starting with a runner on second base, as currently used in MLB, offers a balanced compromise. It reduces extra innings beyond the 10th by 74.2% in MLB and 80.1% in college baseball, with a smaller increase in scoring at 1.58 additional runs per game. These findings indicate that runner-on-base scenarios effectively accelerate game resolution but come with varying impacts depending on the starting base runner condition and the level of play.

CONCLUSION

If college baseball conferences are considering adopting extra-inning rules with runners on base, this study provides a clear understanding of the potential outcomes. Starting extra innings with a runner on second base offers a balanced approach, reducing extra innings beyond the 10th by 86% while increasing runs by 1.58 per game. This option preserves the potential for occasional extended games (e.g., 0.3% lasting 14 innings or more) while maintaining a relatively traditional scoring dynamic.

Alternatively, starting extra innings with bases loaded minimizes game duration, reducing extra innings beyond the 10th by 95% in college baseball. However, this approach significantly increases scoring, adding 3.03 runs per game on average. Decision-makers must weigh the benefits of faster game resolution against the potential impacts on game strategy, player workload, and the fan experience.

Further research could explore how these rule changes affect player performance, fan engagement, and the broader perception of baseball as a strategic and endurance-driven sport. 

DAVID C. HYLAND, PHD, is a professor of finance and sabermetrics at Xavier University, where he also serves as Director of Baseball Analytics for the Musketeers’ baseball program. A SABR member since 2018, he sits on the board for the Frontier League’s Florence Y’alls and is a lifelong fan of the Cincinnati Reds.

Sources

Retrosheet MLB Play by Play Data: https://www.retrosheet.org/.

Macht, Norman L. “Beyond the 9th: The Drama of Baseball’s Marathon Matches.” Peanuts & Crackerjack, January 13, 2024. https://peanutsandcrackerjack.com/blog/baseballs-longest-games, accessed November 27, 2024.

Tourtellotte, Shane. “Beyond the Ninth Inning.” The Hardball Times, May 8, 2014. https://tht.fangraphs.com/beyond-the-ninth-inning/.

Hyland, David C. “Rounding Second: A Probabilistic Investigation of the Major League Baseball Modified Extra Innings Rule.” SABR Baseball Research Journal, Volume 51, Number 2 (Fall 2022), 61–65.

Palmer, Pete and John Thorn. The Hidden Game of Baseball: A Revolutionary Approach to Baseball and Its Statistics (New York: Doubleday, 1984)

Tango, Tom, Michael Lichtman, and Andrew Dolphin. The Book: Playing the Percentages in Baseball (Lincoln, NE: Potomac Books, 2007).

Notes

1. For a discussion of particularly exciting extra innings games see: https://peanutsandcrackerjack.com/blog/baseballs-longest-games.

2. For a discussion of the extra inning environment across years see: https://tht.fangraphs.com/beyond-the-ninth-inning/.

3. The information used here was obtained free of charge from Retrosheet. Interested parties may contact Retrosheet at www.retrosheet.org.

4. This is a combination of both team’s scored runs.