ABOUT THE MODEL
PASR is a model created by Jackson Fambrough using economic principles, that predicts where college basketball recruits will go to school. The predictions cover the top 150 recruits (as determined by Rivals) for future classes. PASR was previously on KenPom.com, but now has it's own site run by Jackson Fambrough. You can contact Jackson with any questions or concerns by email at firstname.lastname@example.org or on Twitter @JackFambrough.
The theory for the model is based on an economic term called utility maximization. Utility, in an economic sense, means the satisfaction or happiness received from consuming a good or service. The model’s main purpose is to show which school will provide a recruit with the highest possible expected utility. Expected utility is divided into two different categories, short-term utility (the 1-4 years the recruit is in school) and long-term utility (the years after they leave the university).
There are several factors that play a part in generating short-term utility, such as winning percentage. A recruit is more likely to go to a school with a higher winning percentage over the past five years because they want to be with a more successful program than a less successful one.
In addition, any recruit coming into college wants to play right away and if there isn’t any immediate playing time available when they get to a school, they might not want to go that school. The more playing time available at a school, the more likely a recruit will want to commit to that school.
A school can also be more attractive to recruits in amenities they offer, like weight rooms and state of the art stadiums because recruits want to play in top-of-the-line facilities. One of the biggest dreams for any young basketball talent growing up is being able to play on television and becoming a star. As such, the more media coverage a team has, the more likely it is that their players will be shown on television, thus increasing the likelihood that recruits will choose that school.
The last short-term variable deals with distance between the recruit and the school. Recruits want to play close to home because the closer they play to home, the less money their family and friends are going to have to pay in order to travel to the recruit’s games. This means schools are more likely to pick up commitments from recruits closer to the school rather than recruits from different parts of the country.
In analyzing the long-term factors, we expect that both graduation probability as well as the academic ranking matter most to a recruit’s parents. Parents typically want their child to get an education, resulting in an eventual degree from a university. The higher the graduation probability of a school, as well as a higher academic ranking, the more attractive to a recruit’s parents, meaning a recruit is more likely to commit to that school. The ultimate dream for any talented kid playing basketball is to play in the NBA; thus, a recruit is more likely to commit to a school that has a reputation of sending players to the NBA.
The school having the best combination of the short-term and long-term factors detailed above will be the school generating the most expected utility or satisfaction for the recruit, which should result in the recruit choosing that school.
In order to model the theory, a probit model is used. A probit model predicts the probability of something happening based upon certain variables. This model takes variables dealing with three categories (recruit characteristics, school characteristics, and the relationship between the school and recruit) and uses them to predict where a recruit will go to school.
Recruit characteristics involve recruit rankings and what position they play. School characteristics include items such as a school’s success athletically and academically as well as age/capacity of their stadiums. The relationship between the recruit and the school involves the geographical location of the school, if the recruit took an official visit to the school, and the relationship between the recruit and the school’s academics.
Essentially, PASR is capable of predicting the chances any school has with any recruit. All it needs is the data for the characteristics mentioned above. For every recruit the probability listed is the probability the recruit will commit to that school, factoring in all schools that have offered the recruit a scholarship.
Now the question is, how accurate is PASR? If you take a look over at the Accuracy Table tab, PASR correctly predicts a recruit's destination 75% of the time and is fairly consistent in all the recruiting classes since 2006.
Something that might be noticed is some recruits chose schools with a less than 5% chance. While this may seem odd or may even lead to questions concerning the model’s validity, several things can help explain it:
1.) The model assumes every recruit acts rationally in their decision making, but sometimes that isn’t always the case.
2.) The model doesn’t account for everything because of lack of information (i.e. playing styles of recruits and high schools fitting into a prospective college).
3.) Recruits occasionally have extremely unique circumstances causing them to commit to a certain school (i.e Bill Walker and his high school eligibility).
PASR can be used as a real world application by providing fans with unique insight into the recruiting process. PASR, using numbers and data rather than feelings and leaks, is a tool that provides the public with another method to predict where a recruit might attend school.