Crazy Game, blows up model, or does it???

What a crazy game the OSU/Wisconsin game was. Using the model as is, we get Wisconsin winning 24 to 10! The key missing stat was Return Yards and Interception Returns. Add those back in and you get OSU winning 35 to 24. Actually, Wisconsin only scored 13 points which is much lower than expected. Any comments as to why their scoring was so low? Bad field position maybe?

http://spreadsheets.google.com/pub?key=pwYkO8gv1ZMhdjSpMq2nnSA&single=true&gid=4&output=html

3 Qbs from last year

Taking the 3 Qbs from last year, which one could be considered "better" when it counts and not better at padding stats during other games.

For a simple analysis, I looked at points per game vs ranked teams and unranked teams. Also, I compared their points per game average vs the conference average. The SEC gave up almost a TD+FG less per game than the Big 12 so the difference is material. Using these simple stats, I would rank them Bradford/Tebow/McCoy. Bradford and Tebow are very close so maybe call it a tie.

It is interesting to see the drop-off in points going from ranked to unranked opp. Next I may do winning vs non winning opp.

This is getting to be a bad habit!

More dismal numbers although the game did live up to the hype at least. Could have used Beanie this year! I just started reading The Hidden Game of Football again by Palmer. Kicking a field goal on the opp 5 yard line is very much frowned upon. May have not made a difference looking at the drive logs but I'll try anything at this point.

As for the losses against ranked opp., I am not in panic mode yet. The Penn State, Texas, and now this USC game could have gone either way. 1 team has to lose and if you can play a top 5 team to a 50/50 chance of winning, I would say you are a good team. What winning % can you expect against top 5 teams realistically? Still, might be nice to win 1 from time to time!

Golf Statistics Analysis

I thought it might be fun to try some golf statistical analysis. The PGA website makes it very easy to download data into excel.

The main statistics I used were driving distance, greens in regulation, driving accuracy, average puts per round, and average score. The driving distance and accuracy combine to make the % greens in regulation and then putting and %greens in regulation drive average score.

Here is the %greens in regulation stats and the average score regression.

What can we learn from this?
Before that, I also graphed totals winnings vs average score.

Wow! That last stroke is what makes Tiger a Tiger.

Let's look at using this information now. Can we advance a player to the "Tiger" line? Let's pick on Jim Furyk for no reason other than he is sponsored by a power company. Lets plot where he is relative to the line that would get him the same score as Tiger.

First we notice something, Jim is very good at getting to the green in regulation. Getting better in that area while certainly possible, is going to be very difficult and probably still won't do much for him. Look at the putting now. Now we have some room to improve. There are many players that have better putting stats than Jim does so it should be easier in relative terms to improve in this area. I plan on looking at Jim's stats since this analysis (2007 data I think) and see if the pattern repeats or not.

I didn't realize how easy it was to get golf stats now. Very interesting data.

Some Fiesta... (not!!)

Well, I at least should be happy that model didn't do too bad. The score was quite a bit lower than expected but the spread was dead on. Still, this is getting a bit old. Maybe OSU should pass more? 7 yard per att. vs 5 per att running the ball?

Link Test

http://spreadsheets.google.com/pub?key=pwYkO8gv1ZMgyj_wn4IpZPA&output=html&gid=0&single=true

I found this chart interesting. It is the On Peak Weekly Average (Day Ahead) for SE Mass in the NE ISO from 2004-2007. It does need to be indexed to natural gas prices in NE but look at 2006, 2007. The exponents of the power law equations of all the years (except 2005) are fairly close. 2005 had Katrina which really spiked the prices at the end of the year. I expect that when I index the prices to nat gas, the chart will be even more behaved. You can also see that the highest and lowest weeks don't quite fit. They could be partitioned out to make the fit better. Also, there could be different ways to analyze this data.