Yesterday I posted part one, describing how we can measure the effectiveness of the Blue Jays' front office. I also analyzed the Roy Halladay trade, which showed that the Jays lost approximately 7.9 games because of the trade. In the post today, I want to look at the remaining meaningful trades made by the front office this year, in mostly chronological order.
To recap quickly, Alex Anthopoulos was promoted on October 3. His first move, trading Halladay, was completed on December 16. Just a week later, on December 23, he made his second significant trade. Looking for a shutdown late-inning relief pitcher, the Mariners believed that Brandon League could help the team as they challenged for a playoff berth this year (that turned out well!). In return, the Jays received Brandon Morrow, a highly anticipated starter with outstanding "stuff". The Mariners drafted him 5th overall in 2006, but never seemed to be able to decide whether to turn him into a starter or try to have him become a closer. Morrow never panned out in his years with Seattle, so he was traded to the Jays. He finally had his breakout season this year, showing occasional flashes of brilliance (see here) while finishing 10-7 with an ERA of 4.49. He ended the 2010 season with a WAR of 1.6, while League struggled, posting a -0.1 WAR. Assuming an "average" replacement for League in the bullpen, the Jays gained 1.5 wins from the trade. The trade was one-sided, even when it was made, that many people wondered if it was part of the Halladay trade (where Cliff Lee ended up going to Seattle), but that was not true. The Jays simply made a great trade, which paid off this year and should pay off for many years to come.
After the two big December trades, Anthopoulos mostly tinkered with the lineup until the season started, with only a couple of minor moves. On January 20, the Jays traded for relief pitcher Merkin Valdez, sending cash considerations to the Giants. Valdez ended the year with a -0.1 WAR. On February 6, AA traded for Dana Eveland, a pitcher with the A's. Eveland ended the year with a -0.8 WAR, but assuming an average replacement player, we have already counted his stats in the Roy Halladay trade (see part one), so we cannot count them again. Eveland pitched poorly enough to be sent to the Pirates on June 1 for Ronald Uviedo, who did not pitch in the MLB this year. There were also some insignificant moves scattered throughout the year where AA acquired minor league players such as Zach Johnson and Casey Fien.
The next significant trade was made on April 15. The Jays acquired outfielder Fred Lewis from the Giants for a player to be named later or cash. Lewis ended the year with a 0.8 WAR, which looks like it helped the team, but we must also take into consideration some other things. The plate appearances that Lewis had would have been given to someone else if he had not been acquired. The likely recipients of the PAs would have been Travis Snider and Dewayne Wise, with most going to Snider. In 319 PAs, Snider had a 0.9 WAR, so if assuming that 319 of the 440 PAs that Lewis had would have gone to Snider and Snider would have a constant performance, those 319 PAs could have been worth 0.9 wins. If we give the other 121 PAs to Wise, who had a 0.1 WAR in 118 PAs, he probably would have produced about 0.1 more wins. So overall, those 440 PAs would have produced about 1.0 wins if Lewis had not been acquired. So the net loss on the trade was actually -0.2 wins.
The final significant trade was made on July 14, when the Jays traded Alex Gonzalez to the Braves for Yunel Escobar and Jo-Jo Reyes (who did not pitch in the majors in 2010 for the Jays). Gonzalez had gotten off to a hot start with 17 home runs in the first 85 games of the year, only hit 6 more for the Braves. This trade was a classic case of selling high, with the 33-year old Gonzalez having a career year, while Escobar was in the doghouse in Atlanta but still had a lot of potential. Before the trade, Gonzalez had a 2.8 WAR, while Escobar only had a 0.9 WAR. However, after the trade, Gonzalez only had a 0.9 WAR while Escobar had a 1.0 WAR. So the net gain of the trade was 0.1 win, and considering the trade was made with the future in mind, turned out very well for this year (and should help out the next couple of years with Gonzalez slowing down and Escobar just hitting his peak).
Now that we have looked at each individual trade, we can see the overall result of the trading. Here is a quick summary:
Halladay trade: -7.9 wins
Morrow trade: +1.5 wins
Valdez trade: -0.1 wins
Lewis trade: -0.2 wins
Escobar trade: +0.1 wins
So the trades had an overall net of -6.6 wins (6.6 more losses). This means that had none of the trades been made, the Jays would have won between 6 and 7 more games this year. Looking at the AL East standings this year:
TB: 96-66 (won division)
NYY: 95-67 (won wild card)
BOS: 89-73
TOR: 85-77
So the Jays would have won between 91 and 92 games, putting them ahead of Boston but still out of the playoff picture. If we want to look at the best case scenario for this year only, then Halladay would not have been traded. If the Jays were going for the playoffs this year, they probably would not have traded Gonzalez, as he was the "rental" player in the deal, or the one that could help his team win now, not in the future. So if we assume only the Morrow trade was made (which may not be true, because the Mariners only traded Morrow after trading for Cliff Lee), the Jays would have gained 9.6 wins. That would have put them between 94 and 95 wins, ahead of Boston, and possibly one game behind or tied with the Yankees. Given that the Yankees somewhat tanked during the stretch in order to draw the Twins in the first round of the playoffs, they probably could have won more games if they were in a "real" race for the playoffs, and not just the division. So, we can say with confidence that the Jays still probably would not have made the playoffs (but you never know, if most of those extra wins had come against the Yankees, maybe they would have!).
Even in this fairly simple analysis, we can see that if the Jays really tried hard to make the playoffs this year, they still would have fallen just a little short, and would be sitting in a far worse position for the upcoming years. Seeing that almost every move this year besides the Halladay trade helped, or at least didn't hurt the Jays, the front office did a good job both strengthening the team now as well as building for the future. The jury is still out on the Halladay trade, and will be for awhile until all of Drabek, D'Arnaud, and Gose have played significant time in (hopefully) the majors. But the overall conclusion of this analysis is that Alex Anthopoulos and the rest of the front office did a very nice job with the trades this year, and hopefully the Jays will reap the benefits in the near future.
Thursday, October 28, 2010
Wednesday, October 27, 2010
Alex Anthopoulos and the Playoffs (Part 1)
This post started out as an analysis of how the Blue Jays could have possibly made the playoffs this year if they had not traded Roy Halladay, but it evolved into something much bigger. I want to look at all of the trades in the last 12 months and determine how well the Jays did in each trade for this year only, and then see if they could have made the playoffs by not trading anyone, or by only making certain trades.
The starting date of this analysis takes place on October 3, 2009, when the Jays, among other moves, fired J.P. Riccardi and promoted Alex Anthopoulos to general manager. This signaled a shift for the strategy of the front office, moving from signing older, high-priced veterans like Frank Thomas and A.J. Burnett to making the scouting staff the largest in the majors and developing the farm system. Although it has only been a year, the results are already palpable, and the future is looking bright for the Jays.
The first major move of Anthopoulus' reign as GM was to trade away Roy Halladay, who was set to become a free agent after this year and would not return to Toronto as he was in search of a playoff-caliber team. It was a difficult task, with one of the franchise's most popular players ever being traded, along with a very small market as many teams could not afford Halladay. Eventually, Philadelphia became just about the only option, which meant that they would have leverage over the Jays. Finally, on December 16, they Jays traded Doc to the Phillies for prospects Kyle Drabek, Travis D'Arnoud, and Michael Taylor. They then traded Taylor to the A's for Brett Wallace, another highly sought after prospect involved in the Matt Holliday trade. Later on this year, on July 29, AA traded Wallace to Houston for yet another prospect, Anthony Gose, who the Jays had been trying to acquire all along, but the Phillies had relented before shipping him to the Astros in the Roy Oswalt trade.
To determine the outcome of this trade (as well as every other trade), I am going to only look at this year's production. So even though the Halladay trade was to acquire prospects that will be major league ready in 2-3 years, I want to see how the trades played out this year. I am going to use the Wins Above Replacement statistic to measure each player's value to his team. Although it sounds simple, it can get complex (as it will in the Halladay trade), because we have to account for both the traded player's WAR as well as the WAR of the players who replaced the traded player.
This year, Roy Halladay had an overall WAR of 6.5, but -0.4 of that was due to offense, so if he had been playing for the Jays (with the DH), he would have had a WAR of 7.3. I am assuming all the of values would remain constant no matter what team the player is on. That was the easy part. Now we need to figure out which pitchers had starts this year that Halladay would have had if he pitched in Toronto. Doc made 33 starts this year, and conveniently, the top 5 starters for the Jays this year (Romero, Marcum, Cecil, Morrow, and Rzepczynski) had 139 starts, with 6 other pitchers recording a combined 33 starts. So presuming that Halladay would have started all of the other combined 33 starts, we can figure out exactly how much the Jays lost when they traded Halladay away.
The six other Blue Jays pitchers who started at least one game this year were (with starts in parentheses): Brian Tallet (5 starts), Jesse Litsch (9), Dana Eveland (9), Brad Mills (3), Shawn Hill (4), and Kyle Drabek (3). If we add up the WAR from each start, we can determine the total wins lost due to the trade. Tallet had an overall WAR of -1.4, but in his 5 starts his WAR was -0.35. Litsch only started, and had an overall WAR of -0.1. Eveland had an overall WAR of -0.8. In Mills' three starts, he had a WAR of 0.13. Hill had a WAR of 0.4, and finally Drabek had a WAR of 0.1. The total WAR for the 33 starts was -0.6, which means that if Roy Halladay were to make the 33 starts instead of these pitchers, the Jays would have won about 7.9 more games.
The Halladay trade was just the first of many trades this year by AA, so tomorrow I am going to post part 2, which will evaluate the rest of the trades and determine whether or not the Jays could have made the playoffs this year with certain trades. (See here for part 2)
The starting date of this analysis takes place on October 3, 2009, when the Jays, among other moves, fired J.P. Riccardi and promoted Alex Anthopoulos to general manager. This signaled a shift for the strategy of the front office, moving from signing older, high-priced veterans like Frank Thomas and A.J. Burnett to making the scouting staff the largest in the majors and developing the farm system. Although it has only been a year, the results are already palpable, and the future is looking bright for the Jays.
The first major move of Anthopoulus' reign as GM was to trade away Roy Halladay, who was set to become a free agent after this year and would not return to Toronto as he was in search of a playoff-caliber team. It was a difficult task, with one of the franchise's most popular players ever being traded, along with a very small market as many teams could not afford Halladay. Eventually, Philadelphia became just about the only option, which meant that they would have leverage over the Jays. Finally, on December 16, they Jays traded Doc to the Phillies for prospects Kyle Drabek, Travis D'Arnoud, and Michael Taylor. They then traded Taylor to the A's for Brett Wallace, another highly sought after prospect involved in the Matt Holliday trade. Later on this year, on July 29, AA traded Wallace to Houston for yet another prospect, Anthony Gose, who the Jays had been trying to acquire all along, but the Phillies had relented before shipping him to the Astros in the Roy Oswalt trade.
To determine the outcome of this trade (as well as every other trade), I am going to only look at this year's production. So even though the Halladay trade was to acquire prospects that will be major league ready in 2-3 years, I want to see how the trades played out this year. I am going to use the Wins Above Replacement statistic to measure each player's value to his team. Although it sounds simple, it can get complex (as it will in the Halladay trade), because we have to account for both the traded player's WAR as well as the WAR of the players who replaced the traded player.
This year, Roy Halladay had an overall WAR of 6.5, but -0.4 of that was due to offense, so if he had been playing for the Jays (with the DH), he would have had a WAR of 7.3. I am assuming all the of values would remain constant no matter what team the player is on. That was the easy part. Now we need to figure out which pitchers had starts this year that Halladay would have had if he pitched in Toronto. Doc made 33 starts this year, and conveniently, the top 5 starters for the Jays this year (Romero, Marcum, Cecil, Morrow, and Rzepczynski) had 139 starts, with 6 other pitchers recording a combined 33 starts. So presuming that Halladay would have started all of the other combined 33 starts, we can figure out exactly how much the Jays lost when they traded Halladay away.
The six other Blue Jays pitchers who started at least one game this year were (with starts in parentheses): Brian Tallet (5 starts), Jesse Litsch (9), Dana Eveland (9), Brad Mills (3), Shawn Hill (4), and Kyle Drabek (3). If we add up the WAR from each start, we can determine the total wins lost due to the trade. Tallet had an overall WAR of -1.4, but in his 5 starts his WAR was -0.35. Litsch only started, and had an overall WAR of -0.1. Eveland had an overall WAR of -0.8. In Mills' three starts, he had a WAR of 0.13. Hill had a WAR of 0.4, and finally Drabek had a WAR of 0.1. The total WAR for the 33 starts was -0.6, which means that if Roy Halladay were to make the 33 starts instead of these pitchers, the Jays would have won about 7.9 more games.
The Halladay trade was just the first of many trades this year by AA, so tomorrow I am going to post part 2, which will evaluate the rest of the trades and determine whether or not the Jays could have made the playoffs this year with certain trades. (See here for part 2)
Sunday, October 24, 2010
Fact of the Week XI: Most and Least Valuable Blue Jays Postseason Games
As the playoffs move past the championship series' and on to the World Series, I wanted to take a quick look at valuable, and not valuable, postseason games by a Blue Jays player and pitcher. Considering the Jays have only played 41 playoffs games ever, and haven't made the playoffs since they won the World Series back-to-back in 1992 and 1993, all of these games will be from the mid-80s and early 90s.
The most valuable game ever by a Blue Jays hitter was by Devon White, in game 4 of the 1993 World Series. His Win Probability Added (WPA) for that game was 0.719, which is extraordinarily high. It is actually the 11th most valuable game ever in the postseason by a player. In the 15-14 Blue Jays win, White went 3-for-5 with 2 runs and 4 RBIs. The big hit came in the top of the 8th inning, with 2 runners on and 2 out, with the Jays losing 14-13. White hit a two run triple to put the Jays on top 15-14, and the play increased the Jays' win expectancy from 24% to 74%.
The least valuable game ever by a Blue Jays hitter was John Olerud, in game 2 of the 1992 World Series. His WPA for the game was -0.273, as he went 0-for-4 with a strikeout. The biggest blow came in the top of the eighth with 1 out and runners on first and third and the Jays trailing 4-3. Olerud popped out to the third baseball, which resulted in a decrease in win expectancy of 16%. Coincidentally, the Jays still ended up winning the game 5-4, in large part because of Ed Sprague, who hit a pinch-hit, two-run home run in the top of the 9th to give the Jays the 5-4 lead. His one at-bat in the game increased the Jays' win expectancy by 67%, which was actually the second-most valuable postseason game by a Jays' hitter behind Devon White.
For pitchers, the most valuable postseason game ever pitched was by David Cone in game 2 of the ALCS in 1992. Cone pitched 8 innings, giving up 5 hits and only one run, and the Jays won 3-1, resulting in a game score of 71 and a win probability added of 0.413.
The least valuable postseason pitching performance was by Todd Stottlemyre in game 4 of the 1993 World Series. This was the 15-14 Jays win in which Devon White had the most valuable position player game. Stottlemyre started the game for the Jays and went 2 innings, giving up 6 runs, and had a WPA of -0.486. Interestingly enough, the fourth worst pitching performance was by Al Leiter, who came in to relieve Stottlemyre, pitching 2.2 innings and giving up another 6 runs. Amazingly, the Jays still won, and in both cases of the least value performances they had comeback victories which overshadowed the bad performances by players.
Hopefully in the next couple of years the Blue Jays will be able to add some players to any of the lists above, which will mean that they made the playoffs again after a long drought.
The most valuable game ever by a Blue Jays hitter was by Devon White, in game 4 of the 1993 World Series. His Win Probability Added (WPA) for that game was 0.719, which is extraordinarily high. It is actually the 11th most valuable game ever in the postseason by a player. In the 15-14 Blue Jays win, White went 3-for-5 with 2 runs and 4 RBIs. The big hit came in the top of the 8th inning, with 2 runners on and 2 out, with the Jays losing 14-13. White hit a two run triple to put the Jays on top 15-14, and the play increased the Jays' win expectancy from 24% to 74%.
The least valuable game ever by a Blue Jays hitter was John Olerud, in game 2 of the 1992 World Series. His WPA for the game was -0.273, as he went 0-for-4 with a strikeout. The biggest blow came in the top of the eighth with 1 out and runners on first and third and the Jays trailing 4-3. Olerud popped out to the third baseball, which resulted in a decrease in win expectancy of 16%. Coincidentally, the Jays still ended up winning the game 5-4, in large part because of Ed Sprague, who hit a pinch-hit, two-run home run in the top of the 9th to give the Jays the 5-4 lead. His one at-bat in the game increased the Jays' win expectancy by 67%, which was actually the second-most valuable postseason game by a Jays' hitter behind Devon White.
For pitchers, the most valuable postseason game ever pitched was by David Cone in game 2 of the ALCS in 1992. Cone pitched 8 innings, giving up 5 hits and only one run, and the Jays won 3-1, resulting in a game score of 71 and a win probability added of 0.413.
The least valuable postseason pitching performance was by Todd Stottlemyre in game 4 of the 1993 World Series. This was the 15-14 Jays win in which Devon White had the most valuable position player game. Stottlemyre started the game for the Jays and went 2 innings, giving up 6 runs, and had a WPA of -0.486. Interestingly enough, the fourth worst pitching performance was by Al Leiter, who came in to relieve Stottlemyre, pitching 2.2 innings and giving up another 6 runs. Amazingly, the Jays still won, and in both cases of the least value performances they had comeback victories which overshadowed the bad performances by players.
Hopefully in the next couple of years the Blue Jays will be able to add some players to any of the lists above, which will mean that they made the playoffs again after a long drought.
Sunday, October 17, 2010
Fact of the Week X: Multi Home Run Postseason Games
In game one of the NLCS last night, Cody Ross hit two home runs off of Roy Halladay. It is extremely hard to hit two home runs in the majors, let alone two against Roy Halladay in the postseason. Ross became only the 5th player to hit two home runs in the first game of a League Championship Series. As a comparison, there have been 10 players with at least 2 home runs in a LCS game two, 8 players in a game 3, 7 in a game 4, 2 in a game 5 or 7, and only 1 in a game 6. This is presumably based upon the starting pitcher on the opposing team, and the ace of the staff will usually pitch games 1, 4 or 5, and possibly 7. The lack of games in the later series games (5-7), is probably due to the lack of overall games (if a series ends in a sweep there will not be any game 5, 6, or 7), the quality of starting pitching, and the strategy of managers playing for one-run innings, so more sacrifice bunts and less at-bats.
Ross also became only the 4th player to hit 2 home runs in a postseason game while batting 8th in the lineup. More impressively, the other three players were all hitting with a position player behind them (a game involving the DH), so Ross became the first player to ever hit 2 home runs with the pitching batting behind him.
Finally, he became only the 19th player to hit at least 2 home runs in his first five postseason games in either the World Series or LCS (18 other players have hit at least two in their first five games in the LDS). All of these statistics are fairly impressive, and become more impressive when you consider that he did it against Roy Halladay, who threw the second no-hitter in postseason history in his last start.
Ross also became only the 4th player to hit 2 home runs in a postseason game while batting 8th in the lineup. More impressively, the other three players were all hitting with a position player behind them (a game involving the DH), so Ross became the first player to ever hit 2 home runs with the pitching batting behind him.
Finally, he became only the 19th player to hit at least 2 home runs in his first five postseason games in either the World Series or LCS (18 other players have hit at least two in their first five games in the LDS). All of these statistics are fairly impressive, and become more impressive when you consider that he did it against Roy Halladay, who threw the second no-hitter in postseason history in his last start.
Thursday, October 14, 2010
Predicting Playoff Series
Now that the first round of the playoffs is over, we are left with only 4 teams: the Yankees, the Rangers, the Phillies, and the Giants. In the American League Championship Series, the Rangers are hosting the Yankees, while in the NLCS the Phillies are hosting the Giants. The Phillies and Yankees are the prohibitive favorites to win their respective series' and advance to the World Series for a rematch of last year. But what are the chances of each of the four teams winning their series?
To calculate the odds of each team advancing, I used three different statistics. The first was the team's regular season record in 162 games, which I converted to a probability between 0 and 1 (each team is actually between 55% and 60%. The second is their Pythagorean Win-Loss record, which uses runs scored and run against to predict what each team's record should have been. I again converted the Pythagorean Win-Loss record to a probability. Finally, I used the team's overall record, combining the regular season record and playoff record through the Division Series', and converted it to a probability. These three estimates will all be used to predict the probability of each team advancing, as three estimates should produce a more accurate result than one. The summary for each team is found below:
Texas Rangers
Regular Season record: 90 wins - 72 losses (win probability = 0.55555)
Pythagorean W-L = 91-71 (0.56173)
Overall record: 93-74 (0.55689) - Won their first round series 3-2.
New York Yankees
Regular Season record: 95-67 (0.58642)
Pythagorean W-L = 97-65 (0.59877)
Overall Record: 98-67 (0.59394) - Won their first round series 3-0.
Philadelphia Phillies
Regular Season record: 97-65 (0.59877)
Pythagorean W-L = 95-67 (0.58642)
Overall Record: 100-65 (0.60606) - Won their first round series 3-0.
San Francisco Giants
Regular Season record: 92-70 (0.56790)
Pythagorean W-L = 94-68 (0.58025)
Overall Record: 95-71 (0.57229) - Won their first round series 3-1.
The next part gets a little tricky, as I used a statistical software package to compare the win probability means. I generated 1000 random data points for each win probability, and each data set had a normal distribution of mean = win probability and standard deviation = 1. These data sets are very close to the standard normal distribution of mean = 0 and standard deviation = 1 (if you want to know more about the standard normal distribution, you can see Wikipedia). What this means is that 68% of the data points will be between win probability - 1 and win probability + 1, and since we are creating two different data sets, there should be some difference between the sets if they have different means. The standard deviations represent the differences in performances, as a team will play a different game every night, and not always perform the same. I used 1000 data points so that I could sufficiently determine whether or not there was a difference between the teams, as the more data points, the more precise the estimate. I then subtracted the two data points for the teams that were playing against each other, and figured out a difference. The difference was "home team" - "away team", so if the difference was >= 0, it meant the home team was "better" than the away team, or in my analysis, that the home team won the series. If the difference of "home team" - "away team" < 0, then it meant that the away team would win the series. Finally, I totaled the differences to determine how many times out of 1000 each team would win the series. I did this for each set of win probabilities (regular season, Pythagorean, and total) to find three estimates to the probability of each team winning a series, found below.
ALCS
Using regular season record: Rangers win series 474 times = 47.4%, Yankees win series 52.6%
Using Pythagorean regular season record: Rangers win series 495 times = 49.5%, Yankees win 50.5%
Using regular season and playoff record: Rangers win series 483 times = 48.3%, Yankees win 51.7%
NLCS
Using regular season record: Phillies win series 527 times = 52.7%, Giants win series 47.3%
Using Pythagorean regular season record: Phillies win series 506 times = 50.6%, Giants win series 49.4%
Using regular season and playoff record: Phillies win series 529 times = 52.9%, Giants win series 47.1%
So in the ALCS, the Yankees win the series between 50.5% and 52.6% (and on average 51.6%) of the time according to this estimate. In the NLCS, the Phillies win the series between 50.6% and 52.9% (and on average 52.07%) of the time. These estimates are slightly lower than some other estimates (like here), but this is probably because as the number of data points increases, the probability of each time tends towards 50%. It is a delicate balance between having enough data points to provide an accurate estimate, but not having too many so that the probability is very close to 50% just because of the large n.
So my picks are that both series should be pretty close, but just like popular opinion, the Phillies and Yankees should prevail and meet in the World Series for a second straight year.
To calculate the odds of each team advancing, I used three different statistics. The first was the team's regular season record in 162 games, which I converted to a probability between 0 and 1 (each team is actually between 55% and 60%. The second is their Pythagorean Win-Loss record, which uses runs scored and run against to predict what each team's record should have been. I again converted the Pythagorean Win-Loss record to a probability. Finally, I used the team's overall record, combining the regular season record and playoff record through the Division Series', and converted it to a probability. These three estimates will all be used to predict the probability of each team advancing, as three estimates should produce a more accurate result than one. The summary for each team is found below:
Texas Rangers
Regular Season record: 90 wins - 72 losses (win probability = 0.55555)
Pythagorean W-L = 91-71 (0.56173)
Overall record: 93-74 (0.55689) - Won their first round series 3-2.
New York Yankees
Regular Season record: 95-67 (0.58642)
Pythagorean W-L = 97-65 (0.59877)
Overall Record: 98-67 (0.59394) - Won their first round series 3-0.
Philadelphia Phillies
Regular Season record: 97-65 (0.59877)
Pythagorean W-L = 95-67 (0.58642)
Overall Record: 100-65 (0.60606) - Won their first round series 3-0.
San Francisco Giants
Regular Season record: 92-70 (0.56790)
Pythagorean W-L = 94-68 (0.58025)
Overall Record: 95-71 (0.57229) - Won their first round series 3-1.
The next part gets a little tricky, as I used a statistical software package to compare the win probability means. I generated 1000 random data points for each win probability, and each data set had a normal distribution of mean = win probability and standard deviation = 1. These data sets are very close to the standard normal distribution of mean = 0 and standard deviation = 1 (if you want to know more about the standard normal distribution, you can see Wikipedia). What this means is that 68% of the data points will be between win probability - 1 and win probability + 1, and since we are creating two different data sets, there should be some difference between the sets if they have different means. The standard deviations represent the differences in performances, as a team will play a different game every night, and not always perform the same. I used 1000 data points so that I could sufficiently determine whether or not there was a difference between the teams, as the more data points, the more precise the estimate. I then subtracted the two data points for the teams that were playing against each other, and figured out a difference. The difference was "home team" - "away team", so if the difference was >= 0, it meant the home team was "better" than the away team, or in my analysis, that the home team won the series. If the difference of "home team" - "away team" < 0, then it meant that the away team would win the series. Finally, I totaled the differences to determine how many times out of 1000 each team would win the series. I did this for each set of win probabilities (regular season, Pythagorean, and total) to find three estimates to the probability of each team winning a series, found below.
ALCS
Using regular season record: Rangers win series 474 times = 47.4%, Yankees win series 52.6%
Using Pythagorean regular season record: Rangers win series 495 times = 49.5%, Yankees win 50.5%
Using regular season and playoff record: Rangers win series 483 times = 48.3%, Yankees win 51.7%
NLCS
Using regular season record: Phillies win series 527 times = 52.7%, Giants win series 47.3%
Using Pythagorean regular season record: Phillies win series 506 times = 50.6%, Giants win series 49.4%
Using regular season and playoff record: Phillies win series 529 times = 52.9%, Giants win series 47.1%
So in the ALCS, the Yankees win the series between 50.5% and 52.6% (and on average 51.6%) of the time according to this estimate. In the NLCS, the Phillies win the series between 50.6% and 52.9% (and on average 52.07%) of the time. These estimates are slightly lower than some other estimates (like here), but this is probably because as the number of data points increases, the probability of each time tends towards 50%. It is a delicate balance between having enough data points to provide an accurate estimate, but not having too many so that the probability is very close to 50% just because of the large n.
So my picks are that both series should be pretty close, but just like popular opinion, the Phillies and Yankees should prevail and meet in the World Series for a second straight year.
Friday, October 8, 2010
Fact of the Week IX: Postseason Debuts
Earlier this week, the 2010 baseball playoffs began. On Wednesday night, the Phillies and Reds matched up, with Roy Halladay finally starting the first playoff game of his career after being traded by the Jays this past offseason. He didn't disappoint, throwing only the second no-hitter in playoff history (after Don Larsen's perfect game in the 1956 World Series), and only allowing one baserunner on a 5th inning walk to Jay Bruce.
On Thursday night, the Giants and Braves faced off in the first game of their Division Series. Tim Lincecum threw a complete game shutout, giving up only two hits and a walk while striking out 14 batters, two away from the postseason record. Both of the games pitched were amazing, and they were actually only two of 37 complete game shutouts in the postseason since 1977 (the first year of the Blue Jays). They were the first since 2007, when Josh Beckett threw a complete game shutout with the Red Sox, and the second and third since 2004.
These two performances have made the first round of the playoffs already utterly breathtaking, and hopefully the next couple of weeks can live up to the potential that Halladay and Lincecum have shown in their first postseason starts.
In fact, the games were actually rated by Game Score as the fourth and fifth best pitched games in the postseason of all time. Halladay's game score of 94 tied Don Larsen, and Lincecum's game score of 96 was only behind three pitching performances: Babe Ruth in 1916 (a score of 97, boy could he pitch!), Dave McNally in 1969 (97), and Roger Clemens in 2000, with a game score of 98.
What made these performances even better was the fact that both pitchers had never pitched in the postseason before. If we look at the greatest pitching performances in a postseason debut, they rank second and third all-time behind Babe Ruth's 1916 performance (which was utterly amazing, a 14 inning complete game in which he only allowed 1 run in a 2-1 win). So these two games were the best postseason debuts for pitchers in almost 100 years. The fact that they happened on back-to-back nights was incredible.
On Thursday night, the Giants and Braves faced off in the first game of their Division Series. Tim Lincecum threw a complete game shutout, giving up only two hits and a walk while striking out 14 batters, two away from the postseason record. Both of the games pitched were amazing, and they were actually only two of 37 complete game shutouts in the postseason since 1977 (the first year of the Blue Jays). They were the first since 2007, when Josh Beckett threw a complete game shutout with the Red Sox, and the second and third since 2004.
These two performances have made the first round of the playoffs already utterly breathtaking, and hopefully the next couple of weeks can live up to the potential that Halladay and Lincecum have shown in their first postseason starts.
In fact, the games were actually rated by Game Score as the fourth and fifth best pitched games in the postseason of all time. Halladay's game score of 94 tied Don Larsen, and Lincecum's game score of 96 was only behind three pitching performances: Babe Ruth in 1916 (a score of 97, boy could he pitch!), Dave McNally in 1969 (97), and Roger Clemens in 2000, with a game score of 98.
What made these performances even better was the fact that both pitchers had never pitched in the postseason before. If we look at the greatest pitching performances in a postseason debut, they rank second and third all-time behind Babe Ruth's 1916 performance (which was utterly amazing, a 14 inning complete game in which he only allowed 1 run in a 2-1 win). So these two games were the best postseason debuts for pitchers in almost 100 years. The fact that they happened on back-to-back nights was incredible.
Sunday, October 3, 2010
National League Playoff Race
The race for the final two playoffs spots in the National League has come down to the final day. Currently, the Giants lead the Padres by one game in the NL West, while the Padres and Braves are tied for the wild card. Tomorrow, the Padres and Giants face off for the NL West title, and possibly a wild card berth, while the Phillies and Braves square off with the Braves trying to get the last wild card spot. It will be an exciting day, with a possible three-team playoff (the first one in history) on Monday and Tuesday. In this post I want to determine the probability of each of the three teams (Giants, Padres, and Braves) making the playoffs and facing the Phillies and Reds.
There are four possible scenarios for tomorrow's games:
Scenario 1: Giants and Braves win, Padres lose
Scenario 2: Padres win, Giants and Braves lose
Scenario 3: Giants win, Padres and Braves lose
Scenario 4: Padres and Braves win, Giants lose
For the purpose of this exercise, I am going to assuming the probability of any team winning any game is .5 (a reasonable assumption, but probably not entirely accurate). So the probability of each scenario above happening is 0.25. In the first scenario, the Giants and Braves will make the playoffs, and San Diego will go home. This means that the probability of the Giants and Braves making the playoffs is 1.0, and the probability of the Padres making the playoffs is 0.0. The second scenario is similar, with the Padres and Giants both making the playoffs (prob. = 1.0) and the Braves going home (prob. of playoffs = 0.0).
The third scenario is where things start to get a little trickier. In this scenario, San Francisco wins, so they have a 1.0 probability of making the playoffs, while the Padres and Braves will face off in a one-game playoff on Monday. So the Padres and Braves will both have a probability of 0.5 of making the playoffs (in each case, 2 out of the 3 teams make the playoffs, so each scenario's probability should add up to 2.0).
The final scenario is the most difficult, as there is now a three-team playoff. The first playoff would be Monday, when the Giants and Padres play (winner goes to the playoffs), and then Tuesday the loser would play the Braves for the final spot. The easiest way to think about this is that in scenario 4, there are "four" possible scenarios given the two outcomes of each of the two playoff games. The four scenarios would work like this: a) Giants win, then Padres win, b) Giants win, then Braves win, c) Padres win, then Giants win, and finally d) Padres win, then Braves win. So in this final scenario, the probability of San Francisco and San Diego making the playoffs is 0.75 (they make the playoffs in 3 of the four cases), and the probability of Atlanta making the playoffs is 0.5 (they make the playoffs in 2 of the four cases).
The final step is to figure out the probability of each team making the playoffs. To do this, we multiply the probability of the team making the playoffs in each scenario with the probability of the scenario.
Probability(Giants making playoffs) = 1*.25 + 1*.25 + 1*.25 + .75*.25 = .9375 = 15/16
Probability(Padres making playoffs) = 1*.25 + .5*.25 + .75*.25 = .5625 = 9/16
Probability(Braves making playoffs) = 1*.25 + .5*.25 + .5*.25 = .50 = 8/16
So overall, there are 16 possible scenarios for the playoffs that will determine the two playoff teams in the next three days. In 15 of them, the Giants make the playoffs (the only way they will not make the playoffs is if they lose tomorrow and the Braves win tomorrow, then they lose consecutive one-game playoffs to the Padres and Braves, and the probability of this happening is 0.54, or 1/16. In 9 scenarios, the Padres will make the playoffs, and in 8 scenarios the Braves will make the playoffs. We can see that the total probability of the teams making the playoffs is 32/16 = 2, so we can see that the probabilities are correct in that two of the teams will make the playoffs.
What does this mean? Basically, the race is a two-team race for the final spot, with one team all but clinched. The Giants are all but assured of a playoff birth, while the Padres and Braves are left fighting for the last spot (almost always the wild card, but potentially the Padres could win the NL West and the Braves could win the wild card, although the probability is only 1/16). The Padres do have a slight edge because they can potentially win the division, while the Phillies have already locked up the NL East, leaving the Braves with only the wild card possibility.
So keep yours eyes on the games tomorrow, the Braves and Phillies play at 1:30 and the Padres and Giants play at 4, so even by 4 pm tomorrow we will have a much clearer picture of the possible playoff spots. It will be an interesting day, and possibly an interesting 2 or 3 days, all to probably get beaten by the Phillies and Reds in the first round.
There are four possible scenarios for tomorrow's games:
Scenario 1: Giants and Braves win, Padres lose
Scenario 2: Padres win, Giants and Braves lose
Scenario 3: Giants win, Padres and Braves lose
Scenario 4: Padres and Braves win, Giants lose
For the purpose of this exercise, I am going to assuming the probability of any team winning any game is .5 (a reasonable assumption, but probably not entirely accurate). So the probability of each scenario above happening is 0.25. In the first scenario, the Giants and Braves will make the playoffs, and San Diego will go home. This means that the probability of the Giants and Braves making the playoffs is 1.0, and the probability of the Padres making the playoffs is 0.0. The second scenario is similar, with the Padres and Giants both making the playoffs (prob. = 1.0) and the Braves going home (prob. of playoffs = 0.0).
The third scenario is where things start to get a little trickier. In this scenario, San Francisco wins, so they have a 1.0 probability of making the playoffs, while the Padres and Braves will face off in a one-game playoff on Monday. So the Padres and Braves will both have a probability of 0.5 of making the playoffs (in each case, 2 out of the 3 teams make the playoffs, so each scenario's probability should add up to 2.0).
The final scenario is the most difficult, as there is now a three-team playoff. The first playoff would be Monday, when the Giants and Padres play (winner goes to the playoffs), and then Tuesday the loser would play the Braves for the final spot. The easiest way to think about this is that in scenario 4, there are "four" possible scenarios given the two outcomes of each of the two playoff games. The four scenarios would work like this: a) Giants win, then Padres win, b) Giants win, then Braves win, c) Padres win, then Giants win, and finally d) Padres win, then Braves win. So in this final scenario, the probability of San Francisco and San Diego making the playoffs is 0.75 (they make the playoffs in 3 of the four cases), and the probability of Atlanta making the playoffs is 0.5 (they make the playoffs in 2 of the four cases).
The final step is to figure out the probability of each team making the playoffs. To do this, we multiply the probability of the team making the playoffs in each scenario with the probability of the scenario.
Probability(Giants making playoffs) = 1*.25 + 1*.25 + 1*.25 + .75*.25 = .9375 = 15/16
Probability(Padres making playoffs) = 1*.25 + .5*.25 + .75*.25 = .5625 = 9/16
Probability(Braves making playoffs) = 1*.25 + .5*.25 + .5*.25 = .50 = 8/16
So overall, there are 16 possible scenarios for the playoffs that will determine the two playoff teams in the next three days. In 15 of them, the Giants make the playoffs (the only way they will not make the playoffs is if they lose tomorrow and the Braves win tomorrow, then they lose consecutive one-game playoffs to the Padres and Braves, and the probability of this happening is 0.54, or 1/16. In 9 scenarios, the Padres will make the playoffs, and in 8 scenarios the Braves will make the playoffs. We can see that the total probability of the teams making the playoffs is 32/16 = 2, so we can see that the probabilities are correct in that two of the teams will make the playoffs.
What does this mean? Basically, the race is a two-team race for the final spot, with one team all but clinched. The Giants are all but assured of a playoff birth, while the Padres and Braves are left fighting for the last spot (almost always the wild card, but potentially the Padres could win the NL West and the Braves could win the wild card, although the probability is only 1/16). The Padres do have a slight edge because they can potentially win the division, while the Phillies have already locked up the NL East, leaving the Braves with only the wild card possibility.
So keep yours eyes on the games tomorrow, the Braves and Phillies play at 1:30 and the Padres and Giants play at 4, so even by 4 pm tomorrow we will have a much clearer picture of the possible playoff spots. It will be an interesting day, and possibly an interesting 2 or 3 days, all to probably get beaten by the Phillies and Reds in the first round.
Friday, October 1, 2010
Fact of the Week VIII: Home Run Records
The Blue Jays have accomplished a couple of notable feats in the past few days with home runs. The first happened on Wednesday night when John Buck homered, his 20th of the season, to become the 6th different Blue Jays to hit 20 home runs this year. They became only the 18th team in MLB history to have six hitters to hit at least 20 home runs each. If Edwin Encarnacion manages to hit two home runs in the last three games of the season (he currently has 18 HRs), the team will become only the 5th team to ever have seven players with at least 20 home runs, joining the 1996 Orioles, 2005 Rangers, 2009 Yankees, and the 2000 Blue Jays. No National League team has ever done it, possibly because they would need seven out of eight of their starting position players to do it as opposed to seven out of nine in the American League. One final note is that Alex Gonzalez, traded to the Braves midseason for Yunel Escobar, had 17 home runs as a Jay (only 6 so far with the Braves) and Escobar (who had 0 home runs as a Brave) has 4 home runs as a Jay. So the shortstop position for the Jays also has hit 20+ home runs, which means that the Jays possibly could have become the first team to ever have 8 players hit at least 20 home runs each (if they hadn't traded for Escobar, which I'm glad they did, and if EE hits 2 homers in the next three games).
On a related note, after Thursday night's blowout of the Twins, in which the Jays hit six home runs, they now have 253 home runs as a team so far this year. That is the 4th highest total in MLB history, trailing only the 1997 Mariners (264 HRs), the 2005 Rangers (260 HRs), and the 1996 Orioles (257 HRs). If they continue their pace of 1.591 HRs per game over the final three games, they should end up with 257.77 home runs on the season, which would be good for third all-time. It is interesting to see many of the same teams on both of these lists (the '97 Mariners also had 6 players with at least 20 home runs). One great home run hitter will not vault your team into the home run record books, the team needs at least 5-6 hitters who all can hit at least 20+ home runs, with usually one or two of those players hitting at least 30 or more (Vernon Wells with 31 and Jose Bautista with 54 so far on this year's team).
One final note of interest: also in last night's game, Jose Bautista hit his 53rd and 54th home runs of the season. The first was an upper-deck grand slam, the second an opposite field home run, the first home run to right field in his career! (Unfortunately HitTracker hasn't quite updated his home run total, so we cannot view his home run scatter plot with the one outlier to right field.) Although pitchers next year may try to beat him with outside pitches, he has again proved that he can hit pretty much anything you throw to home. Sure, he would much rather hit an inside fastball (as the first home run showed!), but if you must continue to pound the outside corner, he will either take a good swing and put the ball into right field, or simply walk. Speaking of walks, Bautista needs one more walk this weekend to become only the 14th player to ever have 50 home runs and 100 walks in one season.
So although this weekend will not bring playoff drama for the Blue Jays, there are a couple of milestones that they could reach with good performances. Hopefully they can win a couple of games, hit a few more home runs, and end 2010 with a bang.
On a related note, after Thursday night's blowout of the Twins, in which the Jays hit six home runs, they now have 253 home runs as a team so far this year. That is the 4th highest total in MLB history, trailing only the 1997 Mariners (264 HRs), the 2005 Rangers (260 HRs), and the 1996 Orioles (257 HRs). If they continue their pace of 1.591 HRs per game over the final three games, they should end up with 257.77 home runs on the season, which would be good for third all-time. It is interesting to see many of the same teams on both of these lists (the '97 Mariners also had 6 players with at least 20 home runs). One great home run hitter will not vault your team into the home run record books, the team needs at least 5-6 hitters who all can hit at least 20+ home runs, with usually one or two of those players hitting at least 30 or more (Vernon Wells with 31 and Jose Bautista with 54 so far on this year's team).
One final note of interest: also in last night's game, Jose Bautista hit his 53rd and 54th home runs of the season. The first was an upper-deck grand slam, the second an opposite field home run, the first home run to right field in his career! (Unfortunately HitTracker hasn't quite updated his home run total, so we cannot view his home run scatter plot with the one outlier to right field.) Although pitchers next year may try to beat him with outside pitches, he has again proved that he can hit pretty much anything you throw to home. Sure, he would much rather hit an inside fastball (as the first home run showed!), but if you must continue to pound the outside corner, he will either take a good swing and put the ball into right field, or simply walk. Speaking of walks, Bautista needs one more walk this weekend to become only the 14th player to ever have 50 home runs and 100 walks in one season.
So although this weekend will not bring playoff drama for the Blue Jays, there are a couple of milestones that they could reach with good performances. Hopefully they can win a couple of games, hit a few more home runs, and end 2010 with a bang.
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