Updated May 2016 As discussed on the "RPI Formula" page, in general the NCAA computes the RPI for Division I women’s soccer without regard to game locations. The one exception is in the bonus and penalty adjustments the NCAA makes to the Unadjusted RPI in order to produce the Adjusted RPI. The adjustments award bonuses for good wins and ties and impose penalties for poor ties and losses, with the bonus and penalty amounts depending in part on whether a team receiving an adjustment played the game at home, at a neutral site, or away. There is a reasonable question whether the RPI formula should make a distinction between home, away, and neutral site games. Some critics have asserted, in particular, that the top teams and teams from the strong conferences have leverage that enables them to play more non-conference games at home than away and that this unfairly skews the RPI in their favor. For Division I men's basketball, the RPI formula makes a game site distinction: In its Element 1 (Team's winning percentage) computation, it treats a home win as 0.6 of a win and an away win as 1.4 wins; and an away loss as 0.6 of a loss and a home loss as 1.4 losses. In baseball, there is a similar distinction except that the weights are 0.7 and 1.3, since home field advantage is statistically less in baseball than in basketball. The question is whether Division I women's soccer should convert to a comparable system. HOME/AWAY IMBALANCESIn order to test whether teams play balanced home/away schedules, I
looked at conferences and regions over the 2007 to 2015 seasons to determine
whether they have game location imbalances.
I found that they do. I also
found a pattern to the imbalances:
Conferences and regions with higher average Adjusted RPIs tend to have favorable home game imbalances and conferences and regions with
lower average Adjusted RPIs tend to have unfavorable home game
imbalances. This is not true in all cases, but it is true on average. The following table shows the relationship, for the 2007 through 2015 seasons, between conferences’ average NCAA ARPIs and conferences’ home game imbalances. It is in order from the conference with the highest average ARPI to the conference with the lowest. The following chart, derived from the above table, shows the relationship between conference strength and game locations: As the trend lines show -- pink for percent of non-neutral games at home and yellow for percent of non-neutral non-conference games at home -- there is a correlation between conference average strength and percent of games at home: Stronger conferences' teams tend to play a higher percent of games at home than weaker conferences' teams. The following chart, derived from the above table, shows the relationship between region strength and game locations: As the trend lines show -- pink for percent of non-neutral games at home and yellow for percent of non-neutral non-region games at home -- there is a correlation between regional playing pool average strength and percent of games at home: Stronger regions' teams tend to play a higher percent of games at home than weaker regions' teams. In summary, there are home/away
imbalances. Further, stronger
conferences and regions tend to have favorable home game imbalances and
weaker conferences and regions tend to have unfavorable home game
imbalances. HOME FIELD ADVANTAGEIS THERE A HOME FIELD ADVANTAGE IN DIVISION I WOMEN'S SOCCER?Given that there are home/away imbalances, the next question is whether there is a home field advantage and, if so, its extent. In order to determine whether there is a home field advantage in relation to RPI ratings, I use my Correlator and performance percentage method of analysis applied to data for the nine seasons from 2007 through 2015. (See the "RPI: Measuring the Correlation Between Teams' Performance and Their Ratings" page for information on the Correlator and performance percentage method of analysis.) In a performance percentage analysis, a percentage of 100% means that a group of teams, on average, is performing in accord with its ratings; a percentage above 100% means that the group of teams, on average, is outperforming its ratings; and a percentage below 100% means that the group is under-performing its ratings. To understand home field advantage, I use the Correlator to compare teams' performance percentage in home games as compared to their performance percentage in away games. I do this for all games regardless of the rating difference between opponents, and also for the most closely rated 5%, 10%, and 15% of games. In the table below, I show the results of this analysis for the Unadjusted RPI. I've chosen the URPI because it is "uncluttered" by the bonus and penalty adjustments and thus gives the cleanest picture of the extent of home field advantage: As the two left-hand columns indicate, this shows home and away teams' performance percentages for the URPI with no adjustment for home field advantage. Looking first at the two right-hand columns in the table, they say that for the most closely rated 5% of games, home teams perform better than their ratings (unadjusted for game location) say they should -- their performance percentage is 120.3% rather than the norm of 100.0% -- and away teams perform more poorly than their ratings (unadjusted for game location) say they should -- at the 79.7% level rather than at 100%. The same is true looking at the most closely rated 10% and 15% of games, as well as for all games. In my experience, the impact of home field advantage probably is best measured by the above 5%, 10%, and 15% numbers. Those are the games in which teams are closely rated enough that the impact of home field advantage has a likelihood of affecting games' outcomes. (For games with larger rating differences, the higher rated team is likely to win regardless of game location) Although the exact numbers vary, the above pattern is the same for every rating system I've run through the Correlator. Simply put, there is a home field advantage in Division I women's soccer; and the advantage affects results in a significant number of games. This, of course, is not surprising. WHAT IS THE EXTENT OF HOME FIELD ADVANTAGE?Knowing that there are home field imbalances and that home
field advantage affects game results in a significant number of games, the next step is to measure the
effect. Since home teams perform as
though their ratings are higher than their NCAA RPI ratings and away teams
perform as though their ratings are lower, this suggests that there should be
an upward RPI adjustment one could add to teams’ ratings when they host games and a matching
downward adjustment to teams’ ratings when they are visitors, such that, with those game-by-game
adjustments, the teams then would perform as a whole in accord with their "Home/Away/Neutral (or HAN) Corrected" ratings -- in other words, their performance percentages would be right around 100% for both home and away games. With that in mind, for the eight years 2007 through 2015, for each rating system, I test a series of HAN Correction amounts, made on a game by
game basis, to reflect game location.
This includes testing the Corrections in games in which opponents are closely rated, to see what level of HAN Correction will produce correlations in which
teams perform in accord with their HAN-corrected ratings.When I test a series of HAN Correction amounts for a rating system, the results converge on a particular value of matching upward and downward corrections at which teams’ performances, as corrected game by game based on game location, match their ratings in closely rated games. For corrections of lesser amounts, home teams still outperform and away teams under-perform their ratings; and for Adjustments of greater amounts, home teams under-perform and away teams outperform their ratings. The following table illustrates how this process works, using the Unadjusted RPI as an example: Each rating system, including each RPI variation, has its own home/away correction amount. This is because each rating system has its own spread between teams' ratings as it moves through the ranks. For some systems, teams ratings are quite compact and for other systems, their ratings are more spread out. The amount of the home/away correction depends on this spread. That being the case, when I evaluate a rating system using the Correlator, my first step is to go through the above kind of analysis to determine the appropriate home/away correction for that system. On the "RPI: Modified RPI?" page, where I report on the performance of each of a number of rating systems, I include the home/away correction amount for each of those systems. |