Dave Dagostino, a college basketball analyst for Full Court, is a former Division I head coach and has also served as a Division I recruiting coordinator in the Big East, Atlantic Sun, and TAAC conferences. A former four-year college basketball player and point guard, he also serves as a Game Analyst for Krossover and is a member of the Association for Professional Basketball Researchers. Dave has been active in scouting since 2008, serving as a national evaluator for Blue Star, Executive Director of Scouting for the Round Ball Journal, and a consultant on international basketball through both DDJR LLC and SINO. You can follow him on twitter @dagostinodm.
This is the first of a series of articles in which Dagostino will apply the latest statistical tools to women’s college basketball, and open new windows on the game for Full Court readers.
During my tenure as a college basketball coach, I took great pride in analyzing and evaluating both team and individual talent. As with any other skill, when you spend 10,000 hours of concentrated effort the ability to evaluate becomes almost intuitive.
I have a developed a predominantly subjective approach with a touch of objective evaluation (numbers and analytics) and social psychology. I analyze these three factors while keeping this core question always in mind: “What do they protect and what do they project?”
This helped keep me from overcomplicating the process, but talent evaluation is at best an inexact science.
For example, people tend to generalize wildly when they are evaluating. I have both made and witnessed some of the major mistakes involved with subjective evaluation, such as thinking our own experiences are typical so we project them on the subject at hand. Often, we believe that what a person did last will play into what she will do next, and finally we all have a a bias toward what we have seen -- or thought we have seen -- with our own eyes.
With all this in mind, I am constantly searching for new ways to understand basketball, and my journey has taken me to the uneasy borders of psychology and economics. Through my research, I have become fascinated with irrationality and the opportunities it can create for those who recognize when it is at work. Our mind plays tricks on us and those who see through the illusion to reality, using other means of evaluation, can use every trick played as an opportunity for gain.
As we all know, there is a lot we cannot see when we watch a game.
At the risk of oversimplifying the process of using analytics to define the game of basketball, I remind you of why we do anything in basketball: to win the game. As we begin to understand analytics and its place in the sport, it is necessary to grasp this critical fundamental concept:
There is a stable relationship between point differential and wins.
If you believe this intitutive and research-proven maxim, then the game of basketball becomes more and more a math problem. For example, at the college level, coaches and fans can ask themselves these three questions at the start of the season, and the answers will tell them a great deal about what they need to do to succeed.
1. How many wins would it take for you to win your league?
2. How many more points would you have to score than give up to win that many games?
3. Which statistics contribute most greatly to point differential?
4. Which statistics don’t contribute as much as you think?
Before we go any further, though, let’s take a look at the word “statistics” and what it means to us. First, it is not a “counting stat.” That is, we’re not looking at a total number of points or rebounds, but rather rates and percentages. The reason? The pace of a game has a tremendous impact on counting stats.
When you select a number goal (i.e., only 12 turnovers) instead of a percentage goal (10% of our possessions) you discount pace and possessions in the game. A team with a slowdown, deliberate style could turn the ball over 10 times but may only be operating with 60 possessions, and thus would actually be inefficient offensively. If we think in terms of numbers this team would make their goal but in terms of the percentage of possessions they turned it over (16.7 %) their efforts would be far less impressive.
What we will be doing in future articles is examining the statistics that most directly affect point differential – we challenge our readers to put some of their cherished, old stand-by, statistics under the microscope to see if they meet this criterion: “Does this stat truly affect whether my team wins or loses?”
Naturally, I will propose a series of statistics formulas, but I will then apply these to real-games scenarios. Over the course of the season, we will be able to see how they work on the court, and make modifications if necessary.
The goal is not to discredit older methods of evaluation but rather to supplement what are usually subjective approaches with carefully considered statistics and applications of those statistics.
All coaches and players take great pride in thinking they can "will" themselves and a team through certain situations, and often it’s assumed that the analytic approach depersonalizes the process of playing and winning games. The fear is that the sport will become objective and without emotion, but it’s not about choosing one side (emotion and rising to the challenge) or the other (statistical analysis) – it’s about blending the two without being victimized by either one.