30 April 2009

My Happy Rankings

This is completely non-analytical. These are the teams I like, no matter the reason.

1. Chicago White Sox
2. St. Louis Cardinals
3. Atlanta Braves
4. Florida Marlins
5. Toronto Blue Jays
6. Kansas City Royals
7. Boston Red Sox
8. Los Angeles Angels of Anaheim
9. Cincinnati Reds
10. Baltimore Orioles
11. Minnesota Twins
12. Tampa Bay Rays
13. Los Angeles Dodgers
14. Arizona Diamondbacks
15. Seattle Mariners
16. San Diego Padres
17. New York Mets
18. Oakland Athletics
19. Philadelphia Phillies
20. Pittsburgh Pirates
21. Texas Rangers
22. Detroit Tigers
23. Chicago Cubs
24. New York Yankees
25. San Francisco Giants
26. Cleveland Indians
27. Milwaukee Brewers
28. Houston Astros
29. Colorado Rockies
30. Washington Nationals

Doing this has pointed out to me that I really have pretty positive feelings about almost every team. I don't dislike any teams; the bottom teams I feel rather neutral about. I also can tell how this has changed over time, so maybe I'll redo this list every once in a while. My feelings are based on so many things, including but not limited to: direction the franchise is taking, the players on the teams, quality of pitching, and regard for defense. Success has obviously impacted the ratings as well, when the Washington Nationals finish last. However, I think it's more the way they succeed that gets me happy.. My top 6 are generally underrated and/or underdogs. I've been very impressed with the Red Sox organization lately (Smoltz, Baldelli, Saito..). The Angels are LA's second team, like the ChiSox here. Speaking of which, the Mets fell to 17 despite that. Cincinnati, Baltimore, Minnesota, and Tampa Bay have exciting young players.

Happiness playoffs (if the season ended now)

AL Central White Sox over AL West Angels
AL East Blue Jays over AL Wild Card Royals

NL Central Cardinals over NL Wild Card Marlins
NL East Braves over NL West Dodgers

AL Central White Sox over AL East Blue Jays

NL Central Cardinals over NL East Braves

AL White Sox over NL Cardinals

If that happens, I promise I'll smile!

12 April 2009

A bit about my projections

My projections are not entirely original work, and I do not claim them to be. I put a good deal of time into them, but it was really just fine-tuning other people's work to my own purposes. I came up with several good ideas on which I lacked the time to follow through, but I hope to add them in the future.

The basic data of my projections are an average of the Bill James, CHONE, Marcel, Oliver, and ZiPS projections, all of which are available on Fangraphs. I converted the raw projections into a rate per AB. I understand that PA would be a better denominator, but not all of my data included PA.

For playing time projections, I did a bit more work. I used the Bill James and Marcel projections as the basic guide (part A), and CHONE, Oliver, and ZiPS, to my eye, too often give an inaccurate idea of playing time, especially for younger players. They project more of what the player could do than what the player will get the opportunity to do. I used MLBDepthcharts.com, which I continued to check throughout the offseason, as the other side of my playing time projections (part B). I used the players' projected roles and converted them into an approximate AB total. I also considered whether a player was injured or is considered prone to injury, thus lowering my projected AB. I then considered their likely place in the lineup, again using MLBDepthcharts.com, and multiplied that projected AB total by a lineup position multiplier, which I obtained by weighting the last five seasons of aggregate MLB data by batting order position (5-4-3-2-1), via baseball-reference.com. I then averaged part A and part B, figuring that preseason expectations are not all going to be accurate, but neither will the projections based a player's history. By combining the player's history with his role at the start of the season, I hoped to obtain a better idea of what that player would contribute during the season. My team offense was based on wOBA.

For pitchers, I did much the same, using IP instead of AB. I used a very rough idea of how many IP to expect from each pitcher, and considered the depth and strength of the bullpen in giving an admittedly arbitrary part B to each reliever. My team pitching was based on FIP.

For fielding, I went for simplicity, using the player's AB total to indicate their playing time. I used the CHONE defensive projections (baseballprojection.com) as one side of my defensive projections. I then gathered each player's UZR/150 and games played at their positions for the last three years, courtesy of Fangraphs. I weighted them according to games played. I then, running out of time, used the CHONE projection and my UZR projection at each position, combined with the expected role of the player, to venture an estimate as to the player's runs cost/saved over the course of the season. For instance, a DH would be mostly 0, but if they were likely to spend some time in the field, then I would give them maybe 10% of their UZR/150 for the position, based on how much time they would likely be a defender. It was very approximate, and I considered players without MLB experience to be league average. If they were expected to play multiple roles (as they usually are), I considered them average at C, 2B, 3B, LF, and RF; slightly above average at 1B; and slightly below average at SS and CF.

I then combined the offensive, pitching, and defensive contributions to come up with estimated Runs scored and Runs allowed totals. I used these to determine a Pythagorean win expectation. I then went through each team's schedule and used the log-5 formula to determine the teams' win expectancies against each other, assuming the Pythagorean expectation to be their natural Winning percentage. I did not assign a winner to each game or series; I simply added the win expectancies accrued from each game to arrive at a projected win total.

And there you have it. I simplified many things, and I have so many ideas for improving the system, but I did this all on Excel with limited time, and could not pursue (yet) all of my ideas. Some of those:

-Creating a more formulaic basis for each player's defensive ratings at each position, taking their defense at every position into account, based on playing time. For instance, a player with 6 games experience at 2B but 300 games at SS would have a 2B rating almost entirely derived from their 2B rating and a positional adjustment. Meanwhile, a player with 100 games each at 2B, 3B, and SS would have a rating based almost half on their 2B playing time, but half based on their other infield ratings. I am tentatively using 250 games (over 3 years) as the cutoff point; a player with 250 games at 2B would use only 2B data, a player with 200 games at 2B would use 2B data for 80% of their projection and then date from other positions, and a player with less than 250 games played total would have the remainder averaged in as league average (0).

-Estimating each players RBI and R based on: their position in the batting order, their wOBA (RBI), their SLG (RBI), their OBP (R), the preceding batters' OBP (RBI), the following batters' SLG (R), and the following batters' wOBA (R). This would require fairly accurately projecting each team's wOBA/OBP/SLG projection for each batting order slot, taking into account the different hitters likely to spend time there. It would also require extensive math work in figuring out what correlations (if any) there are between these rates and the actual scoring of runs. This data may or may not matter for my team projections (I would need to be convinced it was more accurate than using wOBA). However, it would help for individual projections, although mostly in a way that common sense would also do.

-Studying the factors that influence a pitcher's decisions. Do certain types of pitcher get more no-decisions per start than others? How do the relative strengths of the offense and bullpen/defense affect which starts become no-decisions (left with lead, left with deficit)? I don't think I'll have the time to study this in-depth, honestly, for a few years, but it's definitely something I would like to look into.

-Park factors. I'm not sure which projections take park factors into account, and whether they project for a neutral park or for a home park, or even which team they project for free agents (i.e. when did they project?). Thus, we can assume the Rockies and Rangers might score less runs, and maybe the A's and Padres won't score as many.

-Taking replacement level into consideration. A team is not likely to give much time to players who perform significantly below replacement level. It should be assumed that replacement level replacements will be found.

-Age. I considered slightly penalizing teams with an older age by projecting, in the aggregate, that they would receive fewer AB from their starters, and more AB from their bench players and replacement/AAA players than projected. This would account for the likelihood that someone will go down, without unduly penalizing a given player who shows no signs of being frail. Age will catch up to someone, say the odds.

-Finally... considering the effects of trades. It is beyond the scope of what I would be doing, but a serious projectionist could consider the expected records of teams in a continuous manner as the season progresses. He could then project at which point a team would decide that they were out of the running, or that they needed another bat/arm to contend. Then a look at who might be available and what money might be available could lead to a projection as to the level of production a team could reasonably expect to gain/lose from trading. Obviously, projecting specific trades would be difficult, but projecting that the team has roughly a 75% chance of acquiring one of 4-5 available pitchers would help. You could add 75% of the average run-saving production of those pitchers, demoting a lesser performer, in projecting the rest of the season. This would influence the final standings.

I probably skipped some nice ideas that I might detail later. Hopefully someone takes the time to solve some of these issues, and hopefully I do so as well.

06 April 2009

Power Rankings

As a corollary to my projections, I'll add the rankings of each team, 1-30. The projections took their schedules into account, so sometimes a better team may have a worse record in the projected standings.

1 Boston Red Sox
2 New York Yankees
3 Tampa Bay Rays
4 Atlanta Braves
5 New York Mets
6 Los Angeles Angels of Anaheim
7 Philadelphia Phillies
8 Chicago Cubs
9 Detroit Tigers
10 Cleveland Indians
11 Arizona Diamondbacks
12 St. Louis Cardinals
13 Minnesota Twins
14 Seattle Mariners
15 Milwaukee Brewers
16 Colorado Rockies
17 San Francisco Giants
18 Cincinnati Reds
19 Chicago White Sox
20 Los Angeles Dodgers
21 Toronto Blue Jays
22 Kansas City Royals
23 Oakland Athletics
24 Houston Astros
25 Baltimore Orioles
26 Florida Marlins
27 Texas Rangers
28 San Diego Padres
29 Washington Nationals
30 Pittsburgh Pirates

There may be a slight bias towards the American League because I include pitchers' hitting in the National League offensive projections. If/when I figure out an appropriate correction for the rankings, I'll adjust them. That also might mean Atlanta has a better chance against Boston in the World Series. Then again, I assumed no home field advantage in the World Series, so if the AL wins the All-Star Game again, the bias will probably correct for that.

2009 MLB Predictions

I've done quite a bit of work on projections this offseason, which I will detail to some extent later. In the meantime, here are my projections for the 2009 season, based purely on my math:

American League
1. Los Angeles Angels of Anaheim 89-73
2. Seattle Mariners 80-82
3. Oakland Athletics 75-87
4. Texas Rangers 69-93
1. Detroit Tigers 85-77
2. Cleveland Indians 83-79
3. Minnesota Twins 80-82
4. Chicago White Sox 77-85
5. Kansas City Royals 75-87
1. Boston Red Sox 99-63
2. New York Yankees 98-64
3. Tampa Bay Rays 91-71
4. Toronto Blue Jays 74-88
5. Baltimore Orioles 67-95

National League
1. Arizona Diamondbacks 86-76
2. San Francisco Giants 81-81
3. Colorado Rockies 81-81
4. Los Angeles Dodgers 81-81
5. San Diego Padres 70-92
1. Chicago Cubs 89-73
2. St. Louis Cardinals 83-79
3. Milwaukee Brewers 83-79
4. Cincinnati Reds 81-81
5. Houston Astros 74-88
6. Pittsburgh Pirates 68-94
1. Atlanta Braves 93-69
2. New York Mets 91-71
3. Philadelphia Phillies 90-72
4. Florida Marlins 71-91
5. Washington Nationals 67-95

Boston over Detroit, 3-0
New York over Los Angeles of Anaheim, 3-1

Atlanta over Arizona, 3-1
New York over Chicago, 3-2

Boston over New York, 4-3

Atlanta over New York, 4-2

World Series
Boston over Atlanta, 4-2

Analysis and opinion later.