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.

new coal plant tracking

http://www.netl.doe.gov/coal/refshelf/ncp.pdf

Dismal Numbers Indeed Part 2

While doing some other work, I wanted to see the average household income by census region. Being in wonderful East North Central, I focused on that region. This website is the source for my chart.

http://tonto.eia.doe.gov/cfapps/STEO_Query/steotables.cfm?periodType=Annual&startYear=2005&startMonth=1&endYear=2009&endMonth=12&tableNumber=4

Sad, we were a solid 4th and now we are 6th going on 7th. We have had the lowest average growth over the period.

Very Interesting Book!

Someone sent me this link. If you don't read anything else, read Chapter 2. It has some great insights that really made me think. The transistor forecast method was very interesting. My company is in the business of selling electricity which customers don't really use/need (actually lethal if they tried). Also, the comments around rain dances being all about the dance and not the results was also very interesting. It all starts with the ultimate objectives, not the methods to meet the objectives.


http://www.forecastingprinciples.com/Long-Range%20Forecasting/contents.html