May 22, 2006 Leave a comment
I posted a fairly mundane (but interesting to me!) article on the SD of BABIP in DIPS 3.0 on Beyond the Boxscore:
An archive of StatSpeak from its days on MVN
May 18, 2006 4 Comments
On Tuesday night, the Yankees’ comeback was almost squandered by another subpar outing by Mariano Rivera. When Jorge Posada launched his walk-off home run, Rivera’s outing was largely forgotten.
While Rivera bounced back with a 1-2-3 9th last night, I want to take a look at his season. For the first time in a long time, Rivera has not been Mr. Automatic. The Sandman is not putting people to sleep anymore. On the surface, Rivera’s numbers look okay. He’s converted 7 of 8 save opportunities. So he must still be good, right?
On the season, Rivera’s peripherals are way off his usual numbers. After giving up one run in one inning of work last night, Rivera’s ERA is at 3.18. That’s over 0.80 runs higher than his career average. His WHIP is holding steady at 1.41 which is 0.35 higher than his career average. And he’s not striking anyone out. His K per 9 IP is at 4.24, a career low and almost 5 strike outs per 9 innings lower than his stellar numbers last season.
For some reason, hitters are putting the ball in play against Rivera, and it’s producing a cascade effect. More hitters are getting on base, more runners are scoring. We can see this effect through some graphs.
Clearly, Rivera’s K/9 IP is tanking. After a near-career high last year, it’s low. Very low. And as Rivera’s K/9 IP goes down, opposing on-base percentage goes up.
As strike outs go down, more balls are being put into play. Batting average against goes up.
With more runners on base, Rivera’s pitches per inning have also gone up as well.
So what does all of this tell us? Rivera is not as dominant this year as he has been in the past. Some claim this ineffectiveness is due to Torre’s not under-using Rivera this year, but I would like to challenge that contention. So far, Rivera has thrown 17.0 innings. He’s on pace for 72 innings which is definitely in line with his usage over the last three seasons.
Another theory bantered about on the Internet is that we’re witnessing the aging of Rivera much as we are seeing the potential end of the line for Randy Johnson. Again, I disagree with this hypothesis. As the graphs show, Rivera’s drop-off is sudden. As pitchers age, their stats gradually worsen. It’s rare to see a drop-off of 5.00 K/9 IP as Rivera has seen this year. I just don’t buy the aging argument.
The Yankees need Rivera to right his ship, but there is no obvious fix. Is he hurt or sore? It could be. Is he losing his effectiveness? After three years of utter domination following an injury plagued 2002, I find that hard to believe. The trends aren’t positive, and the results haven’t been devestating yet. Only time will tell what happens with Rivera.
May 1, 2006 2 Comments
Maury Brown did an interesting article on The Hardball Times today about the upcoming battle on revenue sharing, with baseballís Collective Bargaining Agreement coming to an end. It seems clear to me that the current model does not work, but I think it can be easily revised to do its job better.
The first question at hand is, what is the point of revenue sharing? In my opinion, the revenue sharing system is supposed to help low-revenue teams get better, and put the money towards payroll. Thus, there are two important parts to the process: (1) Making sure we tax the teams with the most money, and (2) Making sure that teams put as much money towards payroll as possible. Baseball pretty much accomplishes the first already, but I think it isnít doing the best job of doing the second step. Of course, Iím not one to criticize without offering a solution (generally), so let me come up with a solution of my own.
Hereís what I would do:
1. Order the teams in terms of revenue minus average revenues. I did this with the numbers provided by Forbes in its team valuations, though I adjusted it back to account for the luxury tax and revenue sharing. Tax only the teams with above average revenues, and give money to the teams with below average revenues. Fourteen teams, from the Yankees to the Phillies, have above average revenues.
2. To determine how much each team has to share, first subtract their payroll from their revenues. This is done so that we donít over-tax high-revenue teams that also invest in their product. Take the number we plan on re-distributing ($312 million in 2005), and divide it by the total of the sharing teamsí revenues minus payroll ($1.439 billion in this case). Then multiply that by each teamís revenue minus payroll to figure out how much theyíre expected to share. (For example, the Yankees had $384 million in revenues, and a $208 million payroll, so there expected contribution would be (384-208)*(312/1439) = $38 million).
3. To determine how much each low-revenue team should receive, again subtract payroll from total revenue for those teams with below-average revenues. Now, because we want to reward those teams with the highest payroll in comparison to their revenues, divide 1 by their revenues minus payroll. This way, the team that spends the most in comparison to their revenues will also get the most money in revenue sharing. Now multiply that number for the team by $312 million divided by the sum of those numbers for all the teams (.28456). (For example, the Aís had a $55 million payroll and $115 million in revenue, so their share of the pot would be 1/(115-55)*312/.28456 = $18 million.)
AndÖweíre done. If youíve been with me the entire time, I think youíll see that what Iím doing is not only very simple, and also fair. While the high revenue teams give a good chunk of money to low revenue teams, both are encouraged to spend as much as possible. For every $2.5 million of payroll it adds, a team will save (or receive) a little over a million dollars, meaning that you can buy a lot more for a lot less.
This would help a lot in terms of making the burden fairer, and in motivating low revenue teams to spend more. My system identifies the Yankees and Red Sox as two teams that are overpaying by a lot ($38 and $23 million, respectively), because while both have high revenues, they also put a lot of money back into the team. On the other hand, teams like the Astros and Braves are really underpaying (by $12 and $11 million, respectively) because their earnings are much higher than their payrolls indicate, even if theyíre not quite in the same league as the Red Sox and Yankees financially.
My system also better identifies who should be getting more money, and who should be getting less. The Marlins and Twins, who both had respectable payrolls for small-market teams in 2005, would both get $8 million more than they do under the current system. Maybe the Marlins wouldnít have had a fire sale then. On the other hand, teams like Tampa Bay and Kansas City, who have done little to improve even with revenue sharing, would get a lot less, though still a good amount ($12 and $8 million less, respectively).
This certainly is not the end-all-be-all of revenue sharing systems. It was basically a fifteen-minute exercise to come up with the framework and do all the math. Nevertheless, I think itís clear that some simple changes to the revenue sharing system could really improve it quite a bit, and make some great incentives for teams to spend, spend, spend!
Team Revenue Payroll Rev Share MLB Rev Share DSG NY Yankees 384 208 76 38 Boston 259 124 52 29 New York Mets 219 101 24 26 Chicago Cubs 211 87 32 27 Los Angeles 209 83 20 27 Seattle 204 88 25 25 San Fransisco 185 90 14 21 Houston 184 77 11 23 St. Louis 184 92 19 20 Atlanta 182 86 10 21 LA Angels 178 98 11 17 Chicao WS 175 75 18 22 Philadelphia 170 96 -6 16 Baltimore 154 74 -2 -14 Texas 153 56 0 -11 San Diego 152 63 -6 -12 Cleveland 144 42 -6 -11 Washington 141 49 -4 -12 Arizona 132 62 -13 -16 Colorado 129 48 -16 -14 Cincinnati 121 62 -16 -19 Detroit 121 69 -25 -21 Oakland 115 55 -19 -18 Milwaukee 107 40 -24 -16 Toronto 105 46 -31 -19 Pittsburgh 100 38 -25 -18 Minnesota 92 56 -22 -30 Florida 88 60 -31 -39 KC 87 37 -30 -22 Tampa 83 30 -33 -21