Draft Recap: A Primer from Paul DePodesta

Before we get into our breakdown of the Baltimore Orioles's 2008 Amateur Draft, I wanted to pass along an interesting draft-related article posted by Paul DePodesta (former GM of the LA Dodgers and current front office assistant for the Padres) on his blog.

DePodesta does a fantastic job of explaining the "draft process" and how you might go about examining whether a team is effective in its approach to the draft. He followed this up with a more in depth look at the Padres approach to the draft this year. While insightful, it may frustrate some Orioles fans who agree with his general premise that college hitters were the good "get" in this draft class (you'll note our Shadow Draft nabbed two in the first five rounds -- Kieschnick and Crawford -- and our "Rounds 2-5 targets" included 6 more -- Sobolewski, Adams, Mercer, Darnell, Flaherty and Danks).

Altogether, this is a good primer to Draft Grading and should help us all keep in mind a team's general process as well as its specific approach to a particular draft. We'll keep these two issues in mind as we step through the Orioles's draft over the next couple of weeks.

Draft Recap Schedule:
MON (6/23) - Xavier Avery
TUE (6/24) - L.J. Hoes
WED (6/25) - Kyle Hudson
THU (6/26) - Greg Miclat
FRI (6/27) - Rick Zagone

Shortstop Aging Curve

Yesterday, I posted about how age affects fielding performance for second basemen. It was found that:

Ages 27 to 29 are when fielding ability is greatest for second basemen as their efficiency rises and their range has not been greatly compromised.

Today, I will be focusing on shortstops. Again, the main fielding considerations for generating outs are the ability to field efficiently and range. Fielding cleanly or fielding efficiency is a skill that should maximize when the player accrues experience that assists in coordination, but then tapers off when age affects reaction time. Range typically has a shorter learning curve (i.e., first step), but more rapidly deteriorates due to age. Both of these general assumptions should apply to shortstops.

Revised Zone Rating (RZR) will be used as a surrogate for fielding efficiency. This metric assumes there is a given territory that a defender should be expected to cover. Of all the balls that pass through this zone, outs are recorded and compared to the number of chances. This is not ideal as RZR will be effected eventually by decreased range, but it should be rather representative because players are typically moved off positions if they are so unable to defend this standardized area.

Out of Zone (OOZ) Plays will be used to represent range. These plays are those that are made outside of the zone designated to the position. Again, there are potential issues. If a defender is playing next to a player who has great range then the number of OOZ plays he can accrue will probably be reduced. The resulting effect may not be great because several players seasons will be used to determine the aging curve line.

The data was collected from the Hardball Times fielding statistics. Fielding performance was recorded from 2004-2007. Out of Zone plays for each player was divided by the number of innings played and normalized over 162 9 inning games. Ages were then determined and applied to the seasons. Ages with less than three data points were removed from consideration. Only full time players with more than one season at SS during this time period were considered.

Shortstop Fielding Age Curves
The curves depicted to the right show the effect of age on fielding efficiency (orange) and range (black). Each horizontal mark represents five plays for both axises. The RZR line has been normalized for the average number of chances encountered at SS for a full 162 game 9 inning season. For instance, if a player moves from one horizontal line to the next over the course of two seasons then the player has improved or declined by five plays. A single play is worth about 0.6 runs, so each line represents 3 runs saved or lost.

Based on the age classes we have on hand (at least three data points had to be available for each age included), we were only able to include ages 22 to 32 on this graph. As opposed to the 2B curves, these have similar apexes, but differ with where they end up. Shortstop appears to take more skill and athletic ability to play effectively. This comes as no surprise. Also, range deteriorates much more quickly than efficiency, which agrees with the 2B study. Fielding lifespan is much shorter for a SS than a 2B though. Range for a SS seems to peak around 26/27 and fielding efficiency peaks 27/28.


Conclusion
A quick check on Google and I find that Tom Tango did something similar in February. His findings basically agree with my own even though we calculated these in different ways. His calculations predict a decline twice as rapidly as my own. For instance, we both find the same peak, but he finds a decrease of -35 plays from peak to age 32. I find it to be -18 plays. I am not sure which is more appropriate. Perhaps considering my findings and Tango's as a range would be a good idea. That range is worth about one win. Regardless, this trend seems more unmistakable.

Age Curves for 2B Fielding

This will be a continuation of the recent Brian Roberts articles, but also the beginning of a new series. We often mention how certain positions age differently or utilize certain skills that are often age dependent. When it comes to fielding there are two main considerations when it comes to generating outs: the ability to field "cleanly" and range. Fielding cleanly or fielding efficiency is a skill that maximizes when the player has had experience at the MLB level. Aging will affect efficiency, but not to a great extent. Range on the other hand is heavily affected by aging, or that is what I would assume. As a player ages, he should experience decreased ability to cover the same territory or have his reaction time slow.

Revised Zone Rating (RZR) will be used as a surrogate for fielding efficiency. This metric assumes there is a given territory that a defender should be expected to cover. Of all the balls that pass through this zone, outs are recorded and compared to the number of chances. This is not ideal as RZR will be effected eventually by decreased range, but it should be rather representative because players are typically moved off positions if they are so unable to defend this standardized area.

Out of Zone (OOZ) Plays will be used to represent range. These plays are those that are made outside of the zone designated to the position. Again, there are potential issues. If a defender is playing next to a player who has great range then the number of OOZ plays he can accrue will probably be reduced. The resulting effect may not be great because several players seasons will be used to determine the aging curve line.

The data was collected from the Hardball Times fielding statistics. Fielding performance was recorded from 2004-2007. Out of Zone plays for each player was divided by the number of innings played and normalized over 162 9 inning games. Ages were then determined and applied to the seasons. Ages with less than three data points were removed from consideration. Only full time players were considered.

Second Baseman Fielding Age Curves
The curves depicted to the right show the effect of age on fielding efficiency (orange) and range (black). It should be noted that each horizontal mark represents five plays. For instance, if a player moves from one horizontal line to the next over the course of two seasons then the player has improved or declined by five plays. A single play is worth about 0.6 runs, so each line represents 3 runs saved or lost. Fielding efficiency maxes out around age 30 or 31. Range is maximized at age 23 or 24. Ages 27 to 29 are when fielding ability is greatest for second basemen as their efficiency rises and their range has not been greatly compromised.

The curves may be surprising. Well, they are surprising to me. I would expect them to have a much wider range in terms of efficiency and range. What may explain why I do not see this is that the tail end of the curve is being dictated by guys who can still play the position. Players who are not able to field are no longer at those position and would not be able to be included in the study.

Conclusion
Second base appears to be a position of considerable skill. It takes several years before fielding efficiency is optimized. Taking this data into consideration, defensive second basemen are hurt by free agency for the most part (or the organizations who sign them). After the renewal system and arbitration cycles take their turns, defensive minded second basemen hit free agency with their better days behind them. It is more likely that the dropoff is far more severe than depicted on these curves due to older 2B neutralizing the aging effect.

Odds Update: Convergence

Another week goes by and the predicted number of total wins is converging. At one point the range between these methods was 30 wins and now the range is 3 wins. Updated postseason odds for PECOTA and ELO show a slight decrease. PECOTA has the Orioles at 1:106 and ELO has us at 1:22. Here is the new graph:

Does a low bullpen ERA result in more 1 run wins?

This past weekend Rick Maese wrote the following in the Baltimore Sun:

Generally, you notice the impact of a manager in the close games, when the outcome might hinge on a single decision. At this point a year ago, the Orioles were 6-15 in one-run games. They finished the year 13-31.

And this year? With last night's win, the Orioles are 15-9 in one-run games, tops in baseball.

Much of that credit goes to the bullpen and to the fact that one of the first things Trembley did after last season was add pitching coach Rick Kranitz to his staff.

I'm not really sure that this is true. Typically, you hear that winning percentage in one run games is the result of luck. I disagree with that, but only slightly. I imagine that one run record is largely luck, but that general team talent is also a contributor. For instance, I expected a team with a .600 winning percentage to do better than one with a .400 winning percentage, but that due to limited sample size you will often see a "noisy" relationship between total team winning percentage and 1 run winning percentage. Today, I am going to try to test this and figure out if a bullpen is largely responsible for a team's record in 1 run games.

Method
I took data from all AL teams from 2005 to 2007 (3 seasons - 42 data points). I calculated their bullpen ERA+. I used ERA+ instead of plain ERA in order to normalize year to year changes in run scoring. I also calculated their 1 run game winning percentage and overall winning percentage for each team-year. I then related 1 run winning percentage to bullpen ERA+ and overall winning percentage. Additionally, I compared bullpen ERA+ to the % difference between 1 run winning percentage and overall winning percentage.

Results
Bullpen ERA+
Maese's basic assumption (and it really isn't fair to attribute this assumption to Maese as you hear this rabbitted about nearly everywhere) is that a good bullpen ERA results in a good 1 win winning percentage record. What we see when we graph these is that there is an incredibly poor relationship between these two measurements (r2=0.06). This basically means that knowing a team's bullpen ERA essentially tells you nothing about what their winning percentage will be in 1 run games. Can you find the Indians in 2005? They are a solitary dot with an ERA+ of 155 and a win pct of .378. I would say that is an outlier, but the general pattern is also rather bunk. True, there does seem to be a pattern, but either there just isn't enough sample size to determine if the bullpen is the deciding factor or not.

Overall Winning Percentage
I assumed this would be far more relevant to the 1 run winning percentage than the bullpen ERA+ metric. This turned out to be the case and overall winning percentage seems to be much better than bullpen ERA+, but still not a great predictor (r2=0.3).






Non 1 Run Winning Percentage
It also bears season that overall winning percentage would obviously have greater significance for the sole reason that the 1 run games are included in that result. Perhaps a better way to determine the effect is to compare 1 run games to non 1 run games. What we see here is a result that is just as worthless as bullpen ERA+ (r2=0.05). We are basically back to where we started as in we have no idea to what we can attribute 1 run game success.




Can Bullpen ERA+ predict difference in Winning Percentage
Finally, I decided to compare bullpen ERA+ to the difference in 1 run winning percentage and >1 run winning percentage. The result is that we have another two statistics that cannot predict each other (r2=0.08).

Conclusion
Maybe small sample size is distorting what we can measure; but, based on these three AL seasons, bullpen ERA+ and non-1 run winning percentage are poor indicators of success in one run games. Overall team record is helpful, but that is most likely due to the inclusion of the 1 run data within that data set.

Orioles Recap: Oh So Fickle

I typically never run recaps because you can easily find your descriptive columns elsewhere. At Camden Depot we try to go underneath the surface and try to explain how things happened or what will happen. Last night though is a good time to introduce leverage index values and winning probability added. These two statistics are collected by FanGraphs.

Here is the graph from last night's game:



Winning Probability (WP) is shown on the top graph. Essentially, the statistic is based on the probability of a certain result in relation to the score, inning, and outs affects a team's chances to win. This is often expressed as Winning Probability Added (WPA). This try to discern how different players producing certain events affect changes in WP. For instance, Salazar's homerun increased the Orioles' chances of winning by ~40%. His WPA for the single event would be 0.40 and this would be added to his other events during the day. His final WPA for yesterday's game was .321 and was third among Orioles' hitters for the day.

Leverage Index in the bottom graph. Basically, the greater your value, the greater the probability in winning is shifted. For instance, in the top of the first inning, you can see that the leverage decreases as each out is made. In relation to other innings, the leverage is low because the game is tied and there are plentiful opportunities to score runs later. A run in the first has less meaning than a run in the ninth because the opposing team has less opportunity to score that single run back to even it out. Another example would be in the top of the ninth when George Sherrill entered the game. The leverage starts high because the Orioles have a one run lead and the Pirates have no outs. It then becomes greater with Nady's single to right. After LaRoche's home run, the leverage is almost nothing because there is no one on and the Orioles are down by a run.

Hopefully, you found this interesting.