The presence of error in statistics as illustrated by basketball predictions

TrueHoop has an interesting paragraph from this afternoon illustrating how there is always error in even complicated statistical models:

A Laker fan wrings his hands over the fact that advanced stats prefer the Heat and LeBron James to the Lakers and Kobe Bryant. It’s pitched as an intuition vs. machine debate, but I don’t see the stats movement that way at all. Instead, I think everyone agrees the only contest that matters takes place in June. In the meantime, the question is, in clumsily predicting what will happen then (and stats or no, all such predictions are clumsy) do you want to use all of the best available information, or not? That’s the debate about stats in the NBA, if there still is one.

By suggesting that predictions are clumsy, Abbott is highlighting an important fact about statistics and statistical analysis: there is always some room for error. Even with the best statistical models, there is always a chance that a different outcome could result. There are anomalies that pop up, such as a player who has an unexpected breakout year or a young star who suffers an unfortunate injury early in the season. Or perhaps an issue like “chemistry,” something that I imagine is difficult to model, plays a role. The better the model, meaning the better the input data and the better the statistical techniques, the more accurate the predictions.

But in the short term, there are plenty of analysts (and fans) who want some way to think about the outcome of the 2010-2011 NBA season. Some predictions are simply made on intuition and basketball knowledge. Other predictions are made based on some statistical model. But all of these predictions will serve as talking points during the NBA season to help provide some overarching framework to understand the game by game results. Ultimately, as Gregg Easterbrook has pointed out in his TMQ column during the NFL off-season, many of the predictions are wrong – though the makers of the predictions are not often punished for poor results.

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