Study Your Rivals

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6 lessons • 30mins
1
Lessons in Personal Productivity
06:21
2
Analyze Performance Data
04:45
3
Study Your Rivals
05:56
4
Balance Offense and Defense
03:46
5
Be A Team Player
04:28
6
Attack Every Challenge
05:36

Beat the Competition: Study Your Rivals with Shane Battier, ESPN commentator and former NBA player

It was more valuable for me to really study the data on my opponent, to really pare down and understand who a player was, what his strengths were, what his weaknesses were down to the fractional percentage. And I was able to form a great plan of attack. I was known primarily as a very good defender in my time, but I knew exactly how I could guard some of the best players in the world utilizing the data.

Look beyond the surface

Before I really learned analytics I tried to guard a guy, Kobe Bryant, who in my estimation was the toughest competitor that I ever played against. And all I had to rely on was the old eyeball test scouting report. Kobe’s got a really good right hand. You have to keep him out of the painted area. He’s a great finisher. So yeah, any Joe Schmo fan could tell you those things. But after studying and going through the school of analytics, I knew exactly to a tee who Kobe Bryant was. And I knew as a defender trying to stop him Kobe’s worst case scenario and my best case scenario was to make him shoot a pull-up jumper going to his left hand, all right. The average possession of the Los Angeles Lakers in 2008 generated .98 points per possession, .98. So you took the average possession of the Lakers, they were going to score .98 points every time they had a possession. And so Kobe Bryant only shot the left-handed pull-up jumper at a 44 percent clip. So every time that he went left and shot that pull up jumper he was generating .88 points per possession.

Well, that’s a tenth of a point less than the average Laker possession. And so if I could make him do that time and time again, which is a lot tougher to do than to say, I’m shaving off a tenth of a point every single time. I’m actually making him detrimental to his team. And the way you have to look at it is, all these tenths of points, all right, add up. They add up here, they add up here, they add up there. And all of a sudden those tenths of a point become points. And in the NBA as we all know the margin between wins and losses is very, very thin. So those tenths of points matter. And that’s all it really is. It’s no different than playing the stock market. You’re trying to shave percentage points off your risk. And if you can accumulate enough, guess what? You’re going to do very well. And so guarding a guy like Kobe Bryant, understanding exactly who he is, what his weakness is, made me a much better defender and allowed me to stick around the NBA for 13 years.

Stay on the cutting edge

If you walk into any front office of an NBA team, you’ll find a number of software engineers, math majors, computer engineers, all crunching gigabytes and gigabytes, terabytes, terabytes of information on basketball. And all trying to glean, what is good basketball? It was a huge departure from the old days when the front office was usually a bunch of ex-basketball guys and they relied on the old eyeball test. And there’s still that portion of the game and portion of the front office work, but now it is much more data centric and data driven.

The biggest question I had about the analytics and the data always came up when I knew fundamentally that a player’s weakness – let’s say he was a really poor left-hand driver. And so in my mind, I knew okay, my game plan is I’m going to make this guy use his left hand and drive left. But it fundamentally went against our team defensive game plan. And so I had more knock down drag out fights with my coaches saying, “Look, the data says he’s worse going to his left hand.” And they would say, “We don’t play defense that way. We have to keep our group game plan intact.” And so there was always that balance of even though I had the data and the data was proven – it wasn’t like it was bad data, it was false data. Trying to work that in a team concept, a team framework without disrupting the greater whole. And it was always a constant struggle. And did I always go with the team framework? Not always. But it was always a question that I brought up and challenge my coaches to give me a good answer for.