Archive for August 2009

My attempt of an NBA post

August 29, 2009

And this attempt comes in the form of finding out what type of teams are best suited to win. Can an all offense or all defense team be a top team? Can a balanced be a top team? Or must a top team be great at both offense and defense?

To do this, I looked at the ORtg and DRtg of teams from the 2009 season. ORtg is the points a team scored per 100 posessions and DRtg is the points a team allowed per 100 posessions.

The R2 for ORtg to W% is .6467. That is shows that most great teams have a solid offense, but it isn’t a sign of a great correlation. Next up is DRtg.

The R2 here is .7244. So there is a good relationship between a solid defense and wins.

The last thing I did was subtract DRtg from a teams ORtg. Here is the result:

An R2 of .9793. That is amazing.

Essentially, the top tier NBA teams are those with excellent offense and defense. If you are an all defense or an all offense team, you don’t stand a good chance to be a great team. The same goes for those teams that are solid at both offense and defense, but not great (kind of like NO).

Thus, it is no surprise the top four teams last season were the leaders in ORtg-DRtg: CLE, BOS, LAA, ORL. Meanwhile, the best offensive team was Portland while the best defensive team was BOS.

The next step is collecting more data beyond the 2008-2009 season, as well as looking at the ORtg-DRtg leaders and seeing how they fared in the NBA playoffs.


Graphing is Phun; 08/28/09 Predictive Nature of WAR

August 28, 2009

Winning% V Pythag %

The baseball Pythagren Therom (located below) is one of the first formula or statistical measures that any bright eyed, new SABR’ist is bound to found their way too.  As almost any one reading this blog would know the formula predicts team winning percentage based on the runs allowed and runs scored by a given team. By studying the things the team as a whole can control, runs scored and allowed it allows almost any analyst with a calculator to quickly decide which teams have been lucky and unlucky in a simplistic sense based on their predicted winning percentage and their actual winning percentage.

(Runs Scored)^2 / [(Runs Scored)^2 + (Runs Allowed)^2] = Predicted Win %

The graph above compares those two things (Wins %/Predicted Win%) in a basic scatter chart of every team over the course of the last 7 full seasons (2002-2008). As expected the square of the correlation coefficent .8778 provides us with assurance that the pythagreon formula of baseball is in fact a useful and well formed predictive tool for team success.

At the present time WAR is considered the most telling statistic of individual performance available to the general public. However it seems as SABR guided baseball fans we typically only use WAR to discuss individual performance, compare one player to another, debate post season awards, argue who was greater Clemente or Robinson, etc. etc. Yet WAR in its truest sense is a measure of wins above a replacement player and wins as we know are a team accomplishment. So it would seem plausible that by studying the total WAR of every team over the course a season we whould be able to accurately predict the Win/Loss record and the Run Differential of said team. The following graphs are an attempt at doing just that. As was the case above the final Winning percentage of all 30 teams over the course of 7 seasons (2002-2008) will be plotted against the corresponding total team WAR in that season for that team, also we will look at total team WAR against the predicted winning percentage through the baseball pythagreon formula.



As you will notice when studying the first chart the square of the correlation coefficent does not come in at the same level as on the Winning percentage V Predicted Winning percentage plot, however the square of the correlation coefficent coming out to .77 still gives satisfactory assurances of the predictive nature of WAR in terms of team winning percentage. The second chart produces an R^2 of .8325 an even better indicator that team WAR can predict run differential of a team over the course of a season.

What exactly does comparing the sum of all individual achievements in comparison to overall team success actually tell us? To be honest, I do not know exactly. However, it seems to reassure those baseball analysts that adhere to the fact that baseball is an individual sport parading itself as a team sport. As the statistical noise (especially in the defensive metrics) is refined and hopefully someday removed this type of analysis should continue to grow in strength.

Like I said in here, the results aren’t great but with the RD way of looking at a teams success being a pretty widely accepted way of doing things and the WAR V winning % having a pretty close R^2 over 7 years worth of data, I think its safe to say that teams with the guys doing to most individually are going to succeed more often then not then over those teams that play as a “team”. So to sum it up in a few words. Screw comradery, give me talent and production in baseball. In the end this might seem like a very basic concept, that good individual performance ends in good team results. But is it really? Further work needs to be done on this subject, weighting pitching andposition player WAR, figuring out which plays a larger role in overall team success could be incredibly useful in deducing what teams are bound to florish or fail going forward.

Discussion question: What is your current top 5 in the AL MVP voting?

August 27, 2009

Here is an opinion i recently posted after glancin at a couple of stats…

Joe Mauer
Zack Greinke
Ben Zobrist
Justin Verlander
Derek Jeter

HM Evan Longoria, Marco Scutaro, Roy Halladay, Miguel Cabrera,

Also, Mark Teixera may, or may not be in my top 10, i’d need to look much more in depth.

With No Regard for Human Life

August 24, 2009

With my suggestion, some statistically saavy basketball minds created a sister blog to 4PARL. It is WNR4HL, aka, With No Regard for Human Life. For those that love basketball and want a funny yet analytical B-Ball blog, check those guys out. We’ll even let them write about basketball here.

Also, go see Inglorious Basterds. If only it went down like that in real life!


August 23, 2009

That piece by MGL on Inside the Book is really, really good. I reccomend reading it.

Nolan Ryan: Overrated and Underrated

August 21, 2009

To some, Nolan Ryan is the greatest pitcher of all time. 5714 strikeouts, 324 wins, and and seven no-hitters. To others, Nolan Ryan was more mediocre than great. 2795 walks, 4.67 BB/9.

So, where exactly does Ryan stand? Well, somewhere in between.

Lets get his bad traits or trait out of the way. Walks. Walks, walks, walks, walks, and more walks. This was his downfall. A career BB/9 of 4.67 is bad. That’s 4-5 base runners per nine without including hits allowed. Nor was this inflated by bad years when he was young or really old. The lowest BB/9 of his career was 3.26 in 1990.  In fact, it took until his thirtenth season to get a BB rate under 4. Walks alone take him out of the conversation for a top ten pitcher of all time, let alone the best pitcher of all time. Another issue is the wins and no-hitters. If you’re a devout follower of this blog, you probably know wins and no-hitters are worthless in determining someone’s greatness. Yet those are two of the biggest reasons why people call him the best pitcher of all time.

Now, time for the good. He could strike people out. Except for his last season in 1993, Ryan struck out at least eight batters per nine innings in every year of his career. He has a career 9.55 K/9 over the course of 5386 innings. That is unbelievable. Unbelievable. Moreover, he didn’t give up home runs. His 0.54 HR/9 is also very good and he never gave up 1 HR/9 in a season.

Ryan also has a fantastic career 2.97 FIP. Moreover, he was very consistent. From his first full season in 1972 to to 1992 his FIP ranged from 2.28 to 3.22.  Finally, he threw a shit ton of innings. One of the most undervalued aspects of pitching is innings thrown. His longevity and success throughout his late years speaks very well for him.

So, is Ryan the best of all time? No. His WAR/100 IP is 1.57. Good, but it doesn’t even compare to some of the games best. Roger Clemens has a WAR/100  of 2.61 in 4916 innings of work.

However, he still was great. Great K rate, HR rate, and FIP throughout a long career.

So, to the common fan is he overrated. To most stat nerds, he is actually underrated.

How Pitching Lines can decieve

August 21, 2009

Note for all of my fans (everybody can laugh now 🙂 This won’t be going up on because I feel it is kind of unrefined and not statspeak material.

Most of the time when we want to see how a pitcher did in a game, we look at there pitching line…

Take this one for example from tonight’s Orioles game

B Matusz 5.1 7 4 4 0 7 1 91-60

(Taken from
From this you would probably think 7 hits and 4 ER off of a HR… not a great outing. You might also notice the 7 strikeouts, but you’d probably  glance over it seeing the 4   ER…

BUT if you look more closely at the game, he was very very good outside of two innings. In the first he let up a single and two doubles. and in the 6th he let up a single, a double, and a home run… throughout the inning though it looked like he was excerting more effort to pitch…

If you take out those two innings, his line goes to 4 innings, 1 hit, 5 K’s, 0 walks, 0 HRs…

If you look at that from a FIP perspective, it goes from a

(HR*13+(BB+HBP-IBB)*3-K*2)/IP (Formula for FIP from fangraphs website)

(13+0-14)/5.33   -1/5.33-    So a -.19 FIP on a game where it would look like he wasn’t good… so actually according to fangraphs WAR, he had a good outing.

But if you go to my modified  pitching line,

(0+0-10)/4   -10/4   -2.50 FIP, whcih is simply amazing even considering it was 4 innings

But the home run did happen, along with the 1st inning, but Matusz still had a solid game according to things he could control. Though some people do like other metrics outside of FIP, this was a good way to show how sometimes the pitching line is very decieving

AND for Orioles fans, he did look very very good outside of those two innings, from what I saw (2 innings) he was dominating the Rays lineup.

(NOTE- anything that you see in such a small sample size (4 innings, or looking at the whole game 5.33 innings is much to small of a sample size to draw any conclusions from)


NOTE this isn’t the total formula for FIP and is missing one portion that is a league specific factor.