Hacker News Reader

Comment by JackFr | Hacker News Reader

Additionally, it could be that the statistics show a result that is an artifact of coaches decisions. If a particular decision is chosen by the pro coaches extremely rarely, but has a moderately better expected result, it could be that the typical expected result is very poor, but the only times coaches make the decision is when they observe a decisive matchup or a special situation -- making it appear statistically like that decision is the better one. (Like looking at basketball shooting percentages, and wondering why centers don't take all the shots given how high their FG%s are.)

View on HN

That is an excellent point.

Coaches do what they think will be successful. If they can't run the ball well, then they don't run the ball, skewing the data.

Also, tendencies change from week to week. You really need a deep understanding of football to get the right, correct data for that week.

This is why data hasn't dominated football, you need to have tons of knowledge before anything else.

The best use of data is to give coaches a high level picture of what the likely result of a given play call will be (trusting sample size to even it out). You hire a coach to then adjust for those other things. However, if the data says that 99% of the time a given play call will have a specific result, coaches shouldn't try to be the exception (unless the game situation demands it). There is a happy medium here. The Rays have figured it out pretty well with Joe Maddon, the NFL will get there too. No one thinks a machine should call all the plays (yet). We just think machine learning and big data should be leaned on as tools the same way that film is. Don't forget, Bill Sharman was considered a nut for watching film in the 1980s. Imagine coaching without now.

I think it's more than "additionally", your point is the major point.

It's difficult, but data can in fact control for matchups and schemes.

Reply on news.ycombinator.com