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Hoop Vision Coaching Analytics Newsletter

ABOUT:

Welcome to (unofficially) the first ever coaching analytics newsletter. You don't have to look very hard to find coaching newsletters with X's and O's, motivation, and leadership advice. So a newsletter of the analytics variety seemed only right. Some quick info about me: I am Video Coordinator for New Mexico State University men's basketball. I also have run a college basketball analytics blog, Hoop Vision, since my junior year of high school.

The newsletter was conceived with Division 1 coaches and programs in mind. However, I certainly hope it can be of equal use to the D2, D3, high school, and AAU coaches already signed up - as well as the college basketball fans, writers, and stat geeks signed up. Giving away too many analytics secrets is of course bad for business, so this is designed to really be a conversation starter for coaches. If you are interested in talking further or would like some thoughts on how these ideas can be more specifically applied to your level of basketball, please feel free to email me at jsperber@nmsu.edu.

Stats Don't Lie?

In some basketball circles, stats and film are viewed in conflict with each other. Getting away from this mindset is probably the first step in better understanding the use of analytics as a coach.

In a very superficial sense, stats and film both don't lie. They simply describe what happens. If a player scores 40 points in a game, he scored 40 points in a game. We have the box score to prove it. We also have the film to prove it. That's simple enough to accept. However, when we start using advanced statistics it can be more difficult for a coach to understand exactly what is being described. Adjusted defensive efficiency, for example, is admittedly a little more difficult to understand than points. But this is no different than someone with no scouting experience trying to watch film and understand if a player is properly stunting when one pass away at the nail.

Things actually start to get tricky when we use stats and film to predict. The common rebuttal to stats/analytics is that you can find a number to tell you anything. Often times various statistics can even contradict each other. Yet isn't the exact same thing true for film? I can find 10 defensive clips that makes Player X look like the best pick and roll defender in the world. I can also find 10 defensive clips that makes that same player look like the worst pick and roll defender in the world. Problems don't stem from the stats and the film, problems stem from the people using the stats and the film. In both cases, you have to be well trained and extremely cautious when moving from a description (Player X scored 40 points) to a prediction (We need to shrink the floor on all Player X touches in order for our defense to be successful).

A great scout can take a given amount of film and get every last piece of useful information out of it. Along the way discarding the unimportant sequences. A great analyst can take a given amount of data and get every last piece of useful information out of it. Along the way discarding the unimportant numbers. The "new-age" basketball thinker can ideally use both types of information in harmony with one another to even further help the decision making process.

Knowing the limitations of a statistic is extremely important, but there are also some inherent advantages to data based decision making. Stats are "watching" every player for every team during every game. There's not enough time in the day to watch every college basketball game. With the the large number of teams and unique styles of play in NCAA basketball, analytics can help keep track of the 351 team and 4,563 players. This is also true for recruiting. With the rise of AAU sneaker circuits and transfers (discussed in more detail below) stats can help with evaluations when your staff only gets limited time to see a recruit.

For info on my previous role at Nevada last season, see the Reno Gazette's feature.

Psychology Of Marshall Henderson

by Jordan Sperber

In a post from 2013, I took a look at Marshall Henderson and a less talked about part of the "hot hand".

Henderson was considered one of the most fearless scorers in all of college basketball. However, the data shows that he really did have a conscience after all. His stats and decision making following a make were drastically different than following a miss.

COACHING POINT:

Players are very influenced by recent results. A shooter is much more likely to take a bad three after hitting a three previously. The "next play" mentality that coaches preach directly contradicts basic human nature. Showing your guys data and film of this change in behavior could help with accountability.

Click here for the full article!

Five Players, Eight Positions

by Drew Cannon

In a post from 2010, Drew Cannon took a look at lineup construction and how it relates to recruiting.

Small-ball and positionless basketball are prevalent in 2016, but this post was ahead of its time in 2010. Cannon shows how the 5 traditional positions are outdated. Arguing that a player's offensive positon should be separate from his defensive position.

COACHING POINT:

The idea that a player must fit an exact position is a potential market inefficiency in NCAA recruiting. Cannon claims non-traditional prospects lacking a true position can fall thru the cracks and become mid-major stars. This is maybe the best example of "Moneyball" in college basketball.

Click here for the full article!

Transfer Analytics

Sports Illustrated writer Luke Winn has wrote about the rise of the "Up-Transfer". A low-major player transferring to a mid-major or a mid-major player transferring to a high-major are examples of up-transfers.

As the sample size for these transfers increases each year, we can use historical stats to learn more about these potential transfers. In other words, how do players from Conference A perform after transferring to Conference B? Every player is different of course, but gathering all possible information is crucial to making recruiting decisions.

The grad transfer may be the scenario most answerable via analytics. Grad transfers play right away, eliminating the uncertainty of projecting how much a transfer will improve in his redshirt year. They also usually have three years of D1 statistics under their belt.

This off-season in particular featured a few grad transfers that became highly coveted targets despite coming from the lowly conferences. To give a very brief (and admittedly incomplete) example of how data can be a tool for recruiting decisions in your program, take a look at the table below of past MEAC grad transfers:

The sample size here is tiny and there are certainly better ways of evaluating players than the selected statistics. However, you can see that MEAC grad transfers have not performed well in the past.

Truthfully, this example was left incomplete on purpose. We can only give so much information away without losing the competitve advantage that data gives us at New Mexico State. That's the real takeaway: Anywhere there is data, there is an opportunity to get a leg up on the competition - scouting, recruiting, player development, and much more.