#22 Jude Schimmel of Louisville and #25 Gennifer Brandon of Cal chase down a loose ball during the 2013 NCAA Women's Final Four. (Photo by Kelly Kline)
#22 Jude Schimmel of Louisville and #25 Gennifer Brandon of Cal chase down a loose ball during the 2013 NCAA Women's Final Four. (Photo by Kelly Kline)

Numbers Game: The first step beyond the basics

October 21, 2013 - 6:59pm

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.

Game analysis can best be described as a form of complicated simplicity. 

In theory, we are reducing a very complex, emotional game with many variables to a series of mathematical formulas. We further break down the game into derivatives to see if, when we put it back together, the whole is still the same.

As mentioned last time, we don’t see analytics as replacement to subjective evaluations, rather as an additional mechanism to better understand the game. At times, it’s easy to get caught up in the package of speed, size, jumping ability, etc., while analysts can swing to other side, focusing on solely true shooting percentage, turnover percentage, and pure point rating.

On one hand, we have traditionalists who trust what they see and distrust what they don’t see. On the other hand, we have those tied to analytics who take the name, face, and emotion out of it and deal strictly with the visible number to tell the tale of what happened in the game. 

I offer the suggestion that we blend what we see with what we compute so that we are not victimized by either. 

But before we enter the meat of objective game analysis, we must first arm ourselves with the necessary weapons. Rather than impose my opinion of which statistics most influence point differential (which we’ve identified as the single most important statistic), what follows is a bundle of mathematical ideas and analytical concepts to play as this series develops, and then see which ones fit your style and personality best.

I will pull from the facebook comments at the bottom of this article to determine which statistics to add in each of the upcoming articles. We can approach it like our preseason, exercising the muscle between our ears to possibly come up with another way of evaluating both our team and opponent before the season begins.

But as we dive into the following formulas, here are some tips to help navigate the numerical jungle we are about to enter.

·       What do we project and what do we protect?

·       How many wins does it takes to win your league?

·       How many more points do you have to score more than you give up to win x number of games?

·       Which statistics contribute most greatly to point differential?

·       Which statistics are given inflated importance when it comes to point differential?

Keeping that in mind, here are some stats usually not cited by basketball coaches, writers and fans that may actually be a lot more important than points per game, rebounds per game or blocked shots and steals. But before we get to the formulas, here's a brief glossary of terms:

           *: To multiply

            /: Divide by

            ORB: Offensive rebounds

            TmMP: Team minutes played

            TmORB: Team offensive rebounds

            OppDRB: Team defensive rebounds

            TmPOSS: Team possessions per game

            PTS: Points

            FGA: Field goal attempts

            FTA: Free throw attempts

Now to the formulas:

1. True Shooting %:  This stat is intended to measure a player’s efficiency at shooting the ball by combining overall shooting percentage, free throw percentage, and three-point percentage.

 PTS / (2 * (FGA + .44 * FTA))

2. Offensive rebounding %: This is the percentage of available offensive rebounds a player grabbed while she was on the floor.

(ORB * (TmMP / 5)) / MP * (TmORB + OppDRB))

3. Pace Factor:  This estimates the total number of possessions per 40 minutes by a team. 

     40 * ((TmPOSS + OppPOSS) / (2 * (TmMP / 5)))

4. Turnover percentage:  This estimates the number of turnovers per 100 possessions.

TO / (FGA + .44 * FTA + TO)

5. New Assist-to-Turnover ratio: This takes a look at the notion that has transcended basketball that the measure of a good ball handling player is determined by her ability to maximize assists and minimize turnovers through good decision-making, vision, and ballhandling. The formula is

(LgPace / TmPace) x ([A x 2/3) – TOs] / minutes

This statistic takes into account several factors, the most significant of which is that even if the passer does get the ball to the shooter for a successful score, she does get credit for the entire score statistically. Thus, the passer gets 33% * 2 of the credit (or 66% of the score). This stands as a lesser value than that of a turnover, which generally coast a team one point per occurrence.  The stat, refined even further for more accuracy, is adjusted for pace, tempo, and minutes played so both offensive style of play and time on the court are eliminated as independent variables. The result is a numeric value that represents a player’s ability to get positive shot opportunities for her teammates.

Another major factor is shot selection, which takes into account shooting percentage, percentage of shots rebounded off of that particular shot, and percentage of shots taken in relation to the total numbers of possessions.  So, for example, a made shot that also results in free-throw attempt is more valuable than a shot made that doesn’t draw a foul. More commonly, measuring how six-foot shots, long two-pointers, corner threes, and even turnovers can tell us a lot about what kind of a shooter is producing a particular percentage. Of course, there's no formula for shot selection without access to data not readily available, but it's a critical concept when analyzing the efficiency of a particular offense -- and defense, for that matter, because a team that forces bad shots is obviously going to be more effective.

But for those statistics available in box scores of games that have already been played, you can see how they impact the outcome. This is a good way to determine what your magic numbers are for success and failure on both an individual and team level.

Remember, however, the past doesn’t equal the future -- so run several tests. As we continue with these articles, I will expand upon the available formulas and analytical data that may help you see the game from a new perspective.

Feel free, though, to comment below via Facebook about concepts that you feel need to be addressed to better serve you and your team. 

As we move forward, the formulas in this article (and many more) will be put to music to help further explain the validity of maintaining a statistical base to keep your perception and understanding of the game grounded and objective in nature. This is not a replacement for the traditional subjective style of scouting … merely an alternative and a counterbalance. Get your feet wet and good luck!