The Turk was kind enough to share some stats with me, related to the earlier discussion about week 1 stats. I haven't really looked at them yet
and probably won't until later today, but figured I'd share them so everyone can manipulate them to make whatever point they want.
For each of the last six years, this is the preseason top 20 QBs (according to Dodds), the year, their projected passing yards for the season, their actual week 1 paassing yards, their actual passing yards for the season, and their number of games played. Some of these, like 2008 Brady or 2010 Roethlisberger, should maybe be excluded (or not) depending on what you're trying to show. Just quickly eyeballing the stats, it looks like players who outperformed their projections in week 1 tended to outperform their projections for the year, i.e. a big week 1 performance isn't "offset" by lower stats later in the year, but rather their season total is adjusted upward. But that's just an impression from glancing at the list (and maybe my preconceived notions of what I expect the data to show), so I don't know if that's really the case. Anyway:
This is awesome, thanks for this. I took a quick look at some relationships and found some interesting though not entirely surprising results.
Data prep:
Excluded QBs with less than 8 games (8 games is admittedly arbitrary) played and Roethlisberger from last year (Suspended)
Converted season data into per game averages so we have an apples-to-apples comparison for QBs with less than 16 games played
Testing whether we should keep pre-season projections or update for week 1 stats:
Tried to predict actual per game averages based on two approaches: 1)pure pre-season projections and 2) Week 1 stats + pre-season average projection for the remainder of the games.
The regressions had an r-squared (amount of variation explained) of 25.13% for the first approach and 33.77% for the second approach. This indicates that we should be updating pre-season projections for week 1 stats and not stay true to the pre-season projection.
Testing Iggy's hypothesis that there is positive correlation between week 1 out performance and rest of season out-performance:
This is effectively going a step beyond part 2 above and saying that in addition to counting week 1 stats, we should also update our pre-season per game projections. Here I did a regression on week 1 out-performance predicting average out-performance in other weeks. The r-squared was weak at 7.12%, but there is a positive relationship and the coefficient estimate indicated that for each 100 yards you threw above your per-game average based on pre-season projections, you were on average going to throw for an additional 12 in other games above your pre-season per game average.
Using this to update our projections from part 2 above, we come up with an r-squared of 38.49%. Higher than the other two approaches.
Bottom line
Based on this data, not only is a good week 1 performance not predictive of future poor performance, it is slightly predictive of even more good performances (relative to pre-season projections). And Iggy was right.