The Wages of Wins blog has some bad news for most NBA fans:
Here is an interesting factoid about the NBA Finals. Since 1978 (the first year we can calculate Wins Produced) no team has won an NBA title without one regular player (minimum 41 games played, 24.0 minutes per game) posting at least a 0.200 WP48 [Wins Produced per 48 minutes]. Only one team – the 1978-79 Seattle Super Sonics [led by Gus Williams with a 0.208 WP48] – managed to win a title without a regular player crossing the 0.250 threshold. And only four other champions didn’t have at least one player surpass the 0.300 mark. This tells us – and hopefully this is not a surprise – that to be an elite team you must have at least one elite player.
Okay, now let’s connect this factoid to the draft. Since 1995, no player who posted a below average college PAWS40 [Position Adjusted Win Score per 40 minutes] his last year in college managed to post a career WP48 above the 0.200 mark (after five seasons, minimum 5,000 minutes played). So although college numbers are not a crystal ball (and really, college numbers are not perfect predictors of what a player will do in the NBA), it does seem like players who don’t play relatively well in college are not likely to become superstars in the NBA.
In short, if your favorite team doesn’t already have a truly great player, they’re highly unlikely to win a championship. And the odds are that they won’t find the great player they need in the draft.
This article also makes an important point about synthetic stats. PAWS40 is a stat that the Wages of Wins people made up. Its value is solely in its correlation with more tangible measures of success. Many people who are suspicious of quantitative analysis hate stats like these, but the proof is in the pudding. When you have a derived statistic that correlates this closely with something useful to measure (like championships or wins), that statistic carries more value than any of the more organic stats, like rebounds per game, or shooting percentage.
June 17, 2009 at 4:44 pm
Wow, cool analysis. Thanks for pointing to this.