APBRmetrics

[capnhistory]

While those of you with serious math skills work on the real research I just wanted to toss in an observation. Basketball appeals to me as a spectator more than other sports because of its dynamic nature. Something that stood out to me about Ed's initial chart was how large a lead had to be for the home team to have the game locked down. I think about it like this: imagine the home team is clinging on to a four point lead in the games closing minutes. If they are exchanging possessions (i.e. matching the visiting team score for score and stop for stop) to maintain that 4pt lead, then the chart indicates that there's still as much as a 20% chance the visiting team coming back to win with as little as 90 seconds remaining in the game. I know the data are still rough, and the results may change, but to me this indicates that a high number of games ending in this situation could result in a dramatic, come-from-behind, stealing-one-on-the-road, type of win. Again I find that nature thrilling and a large part of why I love this sport. I chose a 4 point lead because, while teams can score that much in an atypical play (foul on a made trey), it essentially represents the lowest "two possession" lead a team can have. By comparison if a home football team has an 8 point lead (again do-able in one possession, but really a two possession lead) what are the odds the visiting team overcomes that deficit in less then two minutes? I imagine they are much smaller than 1-in-5. I would be really interested if Jon had found any numbers regarding this. I can't contribute to the research but I just thought I should share that I found meaning in the discussion so far.
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Throw it down big man!
More expansive basketball babble at a slower pace The Captain of History

[Jon Cohodas]

By comparison if a home football team has an 8 point lead (again do-able in one possession, but really a two possession lead) what are the odds the visiting team overcomes that deficit in less then two minutes? I imagine they are much smaller than 1-in-5. I would be really interested if Jon had found any numbers regarding this. I can't contribute to the research but I just thought I should share that I found meaning in the discussion so far.

As you predicted, it is much more difficult in college football.

With two minutes to go, down 8 points:
The home team came back to ultimately win 6/69=10.2% of the time.
The visitors came back 4/75= 5.3% of the time.

Keep in mind that these are all situations with the point differential. The data does not contain possession information.

[jmethven]

Sorry to bump this topic after 3 years, but has any more research been done in this direction? I know some cool graphs were posted during this year's playoffs at advancednflstats.com.

I think it would be really interesting to combine a win expectancy framework with a box score player rating model like PER, a la what they do over at fangraphs.com for baseball. There are a lot of great players like David Robinson and Kevin Garnett who have been criticized by Bill Simmons and other sportswriters for not being able to take over a game in crunchtime. Using win expectancy could give a sense of how much, say, Kobe Bryant's willingness to shoot the ball at the end of games gives him value over someone like Garnett.