Do warm weather schools possess an advantage in college baseball player recruitment? Intuitively, we can surmise the short answer: how could they not? Given the nature of the sport, we assume that colleges situated in more temperate climates are better equipped for training and development, rendering their teams perennially stronger and thus their programs more appealing to top high school recruits. This explanation seems plausible, and without need for much further consideration. What is not so conspicuous, however, is the scope of the recruitment advantage enjoyed by warm weather schools. Just how advantaged are these programs? That turns out to be the more interesting question based on our review of 77 players for 65 schools during the 2010 recruiting season.
In order to explore this topic empirically, our analysis first controlled for factors other than weather that could influence program desirability in the minds of high school baseball players. We thought the most obvious possible subjective influence was the “reputation factor” of individual college baseball programs. High school players may simply be drawn to one school over another due to the school’s standing as an athletic powerhouse. Since there are no data that measure such subjective assessment by high school baseball players, we eliminated this potential bias in our study by comparing colleges only from the six major conferences: Atlantic Coast Conference (ACC), Big East, Southeastern Conference (SEC), Big Ten, Big 12, and Pac 10. All schools in these conferences represent strong baseball programs, and collectively maintain a stronghold on the high school baseball recruitment scene. In fact, according to Baseball America, schools in these Big-6 conferences laid claim to 77 of the top 100 high school recruits in 2010. 1 Conveniently enough, insofar as our analysis of weather is concerned, these major conferences also (generally) represent six different geographical regions in the United States: Atlantic Coast, Northeast, Southeast, Midwest, Southwest and West (respectively).
By holding school reputation constant in our study, we proceeded under the assumption that there is no palpable driving force swaying a player from one conference to another. Thus, we would expect that any given Big-6 conference would have roughly an equal probability of recruiting a top high school player. By this logic, of the 77 top high school players committing to one of the six major conferences in 2010, approximately 13 would commit to each conference. Of course, theory does not always translate to reality; to the right is the actual distribution.
| CONFERENCE | No. of RECRUITS |
|---|---|
| SEC | 28 |
| Pac 10 | 18 |
| ACC | 14 |
| Big 12 | 12 |
| Big East | 4 |
| Big Ten | 1 |
The SEC and Pac 10 were inundated with high school talent, while the Big East and Big Ten lagged far behind. Our analysis indicates that, given the breadth of this recruitment gap, there is a 99.99% chance that top high school recruits do not choose randomly among these six conferences. 2
But to what extent is weather a factor in creating this distribution? Left is a table that shows the average annual temperatures (degrees Fahrenheit) in cities where each school is situated.
| CONFERENCE | TEMPERATURE |
|---|---|
| SEC | 62.4 |
| Pac 10 | 61.0 |
| ACC | 59.5 |
| Big 12 | 57.4 |
| Big East | 55.2 |
| Big Ten | 49.6 |
As it turns out, average temperature and number of recruits are positively correlated: on average, the number of recruits increases by 1.93 for each increase in degree Fahrenheit. 3 And nearly 85% of the variance in number of recruits can be attributed to changes in temperature.
Since schools located in warmer climates attract a disproportionately larger amount of highly ranked players, we wondered if those teams also perform better on the field. Our analysis found that the quality of one’s recruiting class appears to be linked to program performance. Left is a table showing the average Rating Percentage Index (RPI) ranking at the end of the 2011 season of schools in each Big-6 conference.
When regressed against number of top recruits, we find that nearly 84% of the variance in rank is caused by change in number of top recruits. Thus, transitively, weather and rank are directly correlated: the average temperature of a school can fairly accurately predict the quality of its recruitment class. While these results represent only one year of recruitment, the near perfect correlation observed indicates a pattern that transcends pure chance. In short, warm weather schools have a large advantage in college baseball recruitment and thus in their ability to field high-performing teams each season.
| CONFERENCE | AVG RPI |
|---|---|
| SEC | 37.0 |
| Pac 10 | 44.8 |
| Big East | 129.4 |
| Big Ten | 149.4 |
| Big 12 | 83.2 |
| ACC | 48.0 |
References:
- November 18, 2010. Glassey, Conor and Rode, Nathan. Early Signing Period Wrapup: High School Top 100. ↩
- To evaluate whether this distribution is of real statistical significance, and not just attributable to sampling error (that is, the recruitment distribution in 2010, for whatever reason, was an aberration), we conducted a chi-squared test for goodness of fit. This statistical test assesses the extent to which an observed distribution of outcomes falls into line with the expected distribution of outcomes. In this case, the chi-squared value is 37.1, which equates to the sum of the square of the differences between all observed and expected values, divided by the expected values. Given the fact that we have six subjects (conferences) in this study, any chi-squared value greater than 20.5 indicates less than a .01% chance this distribution occurred by happenstance. ↩
- This finding is drawn from a regression we ran between the aforementioned number of recruits in each conference (response variable) and the average temperature for each conference (predictor variable). The resulting regression equation is as follows: Number of recruits = -.98.3 + 1.93 (average temperature). In addition, what is extremely profound in our findings is a t-value of 10.9. A t-value represents the number of standard errors (or standard deviations) the slope falls above a hypothesized slope of 0; 10.9 standard errors is considered exceptionally large. Thus, if absolutely no correlation between the two variables existed, this slope would virtually represent a statistical impossibility. Moreover, the r-squared value is .848, indicating that nearly 85% of the variance in number of recruits committing to each conference can be attributed to the average weather in that conference. Succinctly put, the relationship between these two variables (i.e., temperature and top recruits per program) is immense and inarguably significant. ↩
