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    by Mark Rippetoe

Entries in Texas Rangers (25)


2011 Texas Rangers: Wins, Attendance, and Playoffs

For two years in a row, my attendance prediction model has come extremely close to predicting the actual attendance at the team's win level.

In 2009's prediction, my model overshot actual attendance by 1.15%. In 2010, it overshot actual attendance by 0.85%.

The model has been updated to include data from 2010.

Again, the model predicts an increase in attendance. At last year's win level -- 90 wins -- the model predicts an average attendance of 33,645 per home game. To fall below last year's attendance level, the model says that the Rangers would have to win fewer than 77 games.

Coming off a World Series appearance, it will be interesting to see how the model holds up for 2011.

Regression Notes

The standard error is down from last year's 2,602 attendees per game to 2,560. The R-square and Adjusted R-Square values are nearly identical to the previous year's -- all three years have been right around 0.90 for both values.

Thanks to the accuracy of last year's prediction, the t Stat and P-value numbers for all three independent variables improved. The growth factor variable (inflation) is still the least significant of the three with a t Stat of 1.415, but again, removing it from the calculations results in larger errors.

Playoff Probability

There were no significant changes to the playoff probabilites for each win level in the AL West. The 90% barrier is crossed at 95 wins, and the 50% barrier is crossed at 91 wins.

I'm keeping it short-and-sweet this time to avoid repeating what I've said in the past. If you'd like to read my previous articles, which are good if you'd like to read about how I constructed my model, check out the links below:

Texas Rangers Win-Curve Part I: Wins vs Attendance

Texas Rangers Win-Curve Part II: Playoff Probability

2010 Texas Rangers: Wins, Attendance, and Playoffs

If you're fascinated by this stuff and haven't read Vince Gennaro's book Diamond Dollars, I strongly encourage you to take a look at it.


2010 Texas Rangers Win-Curve Revisited

In 2009, I published a win-curve that predicted Texas Rangers attendance for a given win level. The Rangers won 87 games, and my win-curve predicted 27,958 attendees per game for that win level. Actual attendance was only 27,641. The difference was 317, only a 1.15% difference.

This season, I updated my data and published another win-curve. The yellow dot on the graph marks the 2009 attendance level, and the red dot marks the 2009 win level.

2010 Attendance Prediction. For a full description, read the original article (link above).

In 2010, the Texas Rangers won 90 games. My win-curve predicted an average home attendance of 31,202. According to ESPN's numbers, the actual average home attendance was 30,928.

The difference of 274 attendees per game translates to only a 0.89% overshoot. The model was more accurate this year than last year.

As the season approaches, I will update the data and issue a new prediction.


McCarthy suffers another stress fracture

Jeff Wilson has reported that Brandon McCarthy has been placed on the 7-day DL in Oklahoma City with a stress fracture of his right scapula. Unbelievable.

Seriously unbelievable. Bones get stronger after stress fractures. It's part of the healing process sometimes referred to as overcompensation (or supercompensation). Bones respond to stress and stress fractures by growing thicker, stronger, and more dense.

This is the third diagnosis of a stress fracture in McCarthy's shoulder. Having been through this twice before, McCarthy's shoulder blade should be plenty strong enough to withstand two months of pitching, but it apparently isn't.


What is believable, though? I see a couple of possible explanations.

The original stress fracture from 2007 simply may not be healed. If this is the case, the cause is likely dietary, but it could be that the injury has never been given sufficient time to heal. Stress fractures often become pain-free well before they are actually healed.

Another explanation is that the problem is not actually a stress fracture. Soft tissue is much more susceptible to re-injury than is bony tissue, and the location of McCarthy's injury is a confluence of soft tissue that literally encapsulates the glenohumeral joint.

The recommendations here are running short.

McCarthy attempted a mechanical overhaul, but it doesn't seem to have accomplished its chief goal despite leading to a sparking ground ball rate at Oklahoma City where McCarthy has been excellent.

At this point, it looks like mechanics aren't McCarthy's real problem. If it isn't his mechanics, the culprit is one of the following: diet, strength/conditioning, and genetics.

Genetics, of course, can not be changed, but the other two can be addressed.

In addressing the diet, there are three things to watch for, and they all go hand-in-hand. The goal is improved bone density so the main focal points are calcium, vitamin D, and pH balance. I am not a dietician or a nutritionist, so I will stop short of making specific recommendations.

In addressing potential strength and conditioning issues that may be contributing to McCarthy's problems, a recently published DVD set contains just about everything anyone would ever need to know ranging from prehab and diagnosis to rehab and high performance.

You (and Brandon McCarthy) should check out Optimal Shoulder Performance.

[[Update: The evidence is apparently quite clear. This is, in fact, a scapular stress fracture. Someone who has seen recent video of McCarthy believes that McCarthy had fallen back into old mechanical habits.]]


2010 Texas Rangers: Wins, Attendance, and Playoffs

In winning 87 games last season, the Texas Rangers drew an average attendance that was nearly what my model predicted for that win level -- predicted attendance: 27,958 per game; actual attendance: 27,641 per game.

For this year's model, there have been no tweaks to the methodology. I have simply added last year's data to the model. For details on my wins-attendance model, click here. It is based on the model presented by Vince Gennaro in his book Diamond Dollars: The Economics of Winning in Baseball.

Here's this year's model of Attendance versus Wins:

2010 Attendance Prediction. For a full description, read the original article (link above).

At 2009's level of 87 wins -- represented by the red dot -- my model predicts the Rangers to crack the 30,000 mark for average attendance at 30,593 per game. The model also predicts the Rangers to maintain last year's attendance level with as few as 73 wins -- represented by the yellow dot.

Regression Notes

The standard error is down from last year's 2,646 attendees per game to 2,602. The R-square and Adjusted R-Square values are nearly identical.

The growth factor variable is slightly more significant than last season, but still seems more significant to the calculations than its relatively low t Stat value (1.326) suggests. Removing it from the regression results in smaller R-Square values and a larger standard error.

Playoff Chances

Using a logistics regression for the past 12 seasons (since the Tampa Bay Rays franchise came into existence), I took a look at the odds of making the playoffs for a given win level. This is based on historical probability rather than a suepr complex mathematic system. For a more in-depth explanation of this process, click here.

Josh Hamilton predicted that the Rangers would win 96 games. Historically, 96 wins gives an American League West team a 94.54% chance of making the playoffs (94.50% across the entire American League).

Team president Nolan Ryan predicted 92 wins. Those four wins dramatically change the team's playoff chances. 92-win AL West teams can expect to make the playoffs 62.77% of the time, while a 92-win team from any AL division can expect to make it 68.44% of the time.

Various projection systems predict the Rangers to win between 81 and 87 games. This represents quite a wide range of playoff chances -- AL West: <0.50% to 8.39%; AL overall: 0.73% to 14.02%.

After about the half-way point in a season, the results from such a logistics regression become fairly meaningless for that season. At that point, the division and wild-card races are taking firm shape, and a daily look at the standings tells a much more complete story.

[Note: When properly applied during the off-season (or at the trade deadline), though, playoff probability added can be used to more accurately estimate a player's true dollar value to an organization. This was to be explained in Part III of my Texas Rangers win-curve series, but I stopped at Part II. I may take another crack at finishing that series this year.]


A new PITCHf/x chart

For a long time, I've been frustrated by spin movement (Magnus effect) charts because they don't genuinely show how much a pitch actually moves. These charts perfectly demonstrate how the spin of the ball changes its path, but they don't show how velocity adds a vertical element to the pitch's movement.

Take this chart for example. These are the pitches thrown by Texas Rangers LHP Derek Holland during September and October of last season.

Texas Rangers LHP Derek Holland's pitches.

Even though they are much slower pitches, Holland's change ups are located in the exact same place on the graph as his fastballs. If his fastball and change up start with the same trajectory, the change up will always cross the plate lower than the fastball. I wanted to capture this on a chart, so I put gravity back into the equation.

Using Gameday's physics data (initial position, initial velocity, acceleration), I calculated how long each pitch was in the air. Keep in mind, though, that PITCHf/x starts at 50 from the plate and ends just in front. The mapped data covers only about 48 1/2 feet.

With the flight time for each pitch, I calculated the drop caused by [sea-level] gravity. After converting this number from feet to inches, I added the vertical spin movement. Here's how it turned out:

Texas Rangers LHP Derek Holland's pitches on the gravity chart.

Success. The change ups now appear below his fastballs. The chart reflects not only gravity's effect on a pitch, but it also helps separate pitches by velocity, making identification a little bit easier.

This chart does not replace virtualizations by any stretch of the imagination, but I think it does show how different two pitches can be from each other even when spin movement alone can't show it. Taking this a step further could lead to a "hitter's decision" chart that would represent how different the pitches look at a certain time or distance from the plate.

The gravity charts are now available for all pitchers in's PITCHf/x Database.

[[Update: On April 24, 2010, the Spin Movement w/Gravity charts were updated to reflect gravity's effect from y = 40 to y = 1.417. This change was made based on the information that can be found at Alan Nathan's PITCHf/x site: MLB Extended Gameday Pitch Logs: A Tutorial]]