System Dynamics with NetLogo

About 1 year ago I purchased an old book by Jay Forrester on dynamic systems. I really enjoyed the book and looked around for more information on modeling these systems. I ran into NetLogo . It has some basic models already built and it works on Macs and PCs well. My favorite model is the wolf, sheep, and grass simulation. Basically, the grass grows, the sheep eat the grass, and the wolves eat the sheep. Parameters are grass regrow speed, breeding dynamics, and how much energy is gained by eating. Each animal has an energy level that is reduced each "tick" of the simulation. If it runs out of energy, it dies. You can also change the starting # of sheep and wolves.

They have the simulation setup so if you run it, it eventually settles into a volatile but sustainable steady state between grass, wolves, and sheep.

The interesting part is changing some of the parameters to see what happens. When you do, you quickly learn that intuition takes a holiday in system dynamics. For instance, I changed how much energy the sheep gain from eating grass from 4 to 5 (for whatever reason, they improve their utilization). What do you think happens in the steady state values of grass, sheep, and wolves? I expect that the same amount of grass would sustain more sheep and wolves due to the sheep extracting more energy every tick.

Turns out, the wolf population goes down! Change the sheep gain to 6 and the wolves die off totally after a while. What happens is the additional sheep create increasingly large oscillations in the wolf population that eventually wipes them out. Certainly gave me something to think about.

Bowl Game Predictions

http://spreadsheets.google.com/ccc?key=0As1wRbiaO_HWdHpzNmdVU2pMUkJjSkE0c213SnBTV2c&hl=en



Using a simple system to adjust each teams scoring and defending (based on ranked teams played), I average a teams scoring with another teams defending to get the scores. These are the bets I would have placed using same money on each bet. It may be worthwhile to bet more on games with more disagreement with my forecasts.

I was shocked as to how close the Cin/FLA game was and how favored my model said Penn State is.

Really SOur Grapes..

Unreal, Browns lose with no time left. A few things I really don't like although I don't have any data for it.

• I hate the Pro pass interference penalty, give me college anyday. Roughing the passer is 15 yards but some bs judgement call by a referee during a hail mary pass is good for 40 yards? Please... It puts too much of the game in the referees hands. If someone intentially hits someone in the endzone, flag them for a personal foul, that would make it 25 yards and an automatic first down. That's more than enough.
• I hate the prevent defense or if you must, at least the 3 man rush. Why not just drop everyone back in coverage? What would be the difference? Rush 4 at least everytime.
• And for the final lethal combination, being ultra conservative even though you can't run the clock out anway. Make 2 rushes for .5 yards and then throw on 3rd down anyway? Why not throw when they don't expect it? Or just run your normal offense? Anything but the 3 and out guarentee.

Think of this end game the next time someone goes for it on 4th down.

The offense looked good at least. It would be nice if they can keep that going.

Sour Grapes?

After having to listen to how good Cincinnati (college in this post) is and how they would "kill" Ohio State if they played this year, I decided to do some analysis. Basically, how do you rate teams in college FB when they play such a wide variance of team skill levels? I didn't have much time but through the magic of Export to Excel (IE8) and http://www.cfbstats.com/ , I was able at least get some idea how you could levelize teams using some hard numbers. I settled seeing how teams performed against "ranked" teams vs how well they did against unranked teams. Some teams only play 1 ranked team a year so you have an individual team analysis won't work. Instead, I used split data from 2004-2008. The overall average was that teams score about 11 points less against ranked teams than their average against unranked teams. Ranked teams score about 11-12 points more than their average defense when facing unranked teams. The more ranked teams a team plays, the range of outcomes gets smaller and the impact is less.

I then adjusted Cincinnati and OSU's offensive and defensive average scores/game to levelize them to ranked teams. Since OSU played 3 ranked teams this year, the adjustments to their numbers was less. I then created points scored by each team as an average between their expected offensive points and the opposing defensive allowed points for each team. As it turns out, it is actually pretty close with a slight advantage to OSU. Still, this analysis could be improved but doing a strength of schedule analysis or maybe using vs Winning Teams split data.

Overall, I was a little shocked as to how teams can pad their stats by playing unranked teams. It can make a real difference when comparing teams. I also love the cfbstats website, great work.

Here are the data and analysis links
http://spreadsheets.google.com/pub?key=tyxi63nAKZsVaJ6jptw1rjg&output=html

http://spreadsheets.google.com/pub?key=tQHvnEBJUAVGj80yzkC74nw&output=html

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!