As a follow up to my last post, which shows how improving a team through hitting or pitching is equally valuable, I wanted to look at how a team should improve their hitting. There are different ways to score runs, and because higher run totals lead to higher win totals, I want to figure out what is the best way to improve a team's hitting performance, thus leading to more runs and accordingly more wins.
Similar to last post, I am going to run a linear regression model, except this time I am going to use "Runs scored" as the dependent variable. Why not simply use "Wins"? If we were to run a regression model with hitting statistics as predictors and Wins as the dependent variable, we will have a much higher standard error, which means that the R2 value will be much lower, showing the the variability in wins is not explained very much by the hitting statistics. So if we have runs as the DV, we need appropriate hitting statistics for the independent variables. This is trickier to figure out then expected, as we cannot have statistics that are correlated with each other, or the regression model will experience "multicollinearity". What this means is that although the overall regression will predict the dependent variable nicely, we will not be able to tell which independent variables are accounting for the variability in the dependent variable. Although this my sound hard to prevent, it can be fairly straightforward, as a quick example will show. If we were to use on-base percentage, slugging percentage, and on-base plus slugging percentage as predictors for runs (or wins), our equation would have a multicollinearity problem. The overall regression may result in a low p-value, showing that we have predicted runs well, but each statistic individually would have a high p-value. We would not be able to tell which statistic is heavily influencing runs as OPS is an extraneous variable, and since OPS is basically measuring what OBP and SLG are already measuring, the best course of action is to remove it from the equation.
This regression demonstrates the collinearity issue:
Estimate Std. Error t value Pr(>|t|)
Intercept -5.8651 0.2167 -27.070 <2e-16 ***
OBP 22.4591 17.5672 1.278 0.202
SLG 15.0106 17.5936 0.853 0.394
OPS -4.2768 17.5879 -0.243 0.808
R2 = 0.9089
In short, we are going to need to carefully pick our independent variables so they do not experience collinearity. I am going to use the following statistics to try and predict runs: OBP, SLG, and stolen base %. Although there are many different statistics to use, I am using these three because they represent the three basic ways to improve your team's hitting: get on base more, hit for more power, or become a more successful team running the bases. When we run the regression we get the following:
Estimate Std. Error t value Pr(>|t|)
Intercept -6.0689 0.2246 -27.022 < 2e-16 ***
OBP 17.9231 0.9630 18.612 < 2e-16 ***
SLG 10.7619 0.4947 21.756 < 2e-16 ***
SBperc 0.3956 0.1387 2.852 0.00463 **
R2 = 0.9111
So these three factors explain over 91% of the variability in Runs per Game. Although the R2 value is only slightly higher than the first regression that involved OPS, we can see that all of the statistics are now significant, as opposed to none of the statistics being significant. All three factors have significant effects on runs per game, and OBP has the largest effect. A ten percentage point increase in OBP (e.g. from .350 to .360) is associated with a 0.0179 increase in runs per game. A ten percentage point increase in SLG (e.g. from .450 to .460) is associated with a .0108 increase in runs per game. Finally, a one percentage point increase in SB% (e.g. from 70% to 71%) is associated with a 0.00396 increase in runs per game.
So what does this mean? The best way to increase your team's hitting is to try and score more runs per game. The best way to score more runs per game is to increase OBP. So the best way to increase a team's hitting is to acquire players that will get on base more often, whether it be through a hit, a walk, or a hit-by-pitch. Acquiring players that hit for power will also positively impact a team's hitting, but not as much as players that get on base. So if a team had a limited budget and could only acquire one or two significant players, they should try and acquire those players that can most improve their team's OBP.
Showing posts with label hitting. Show all posts
Showing posts with label hitting. Show all posts
Thursday, November 25, 2010
Friday, August 20, 2010
Fact of the Week II: Home Runs
We all know that Jose Bautista has been having a great season. He is currently leading the league in home runs with 37, 6 more than any other player. But we probably didn't realize just how great of a season it has been in comparison to all other Blue Jays seasons.
Only eight times has a player for the Blue Jays hit at least 40 home runs, and only six players have done it (Carlos Delgado did it three times, in 1999, 2000, and 2003). Bautista already has 37 home runs, and is on pace for 50, which would be the first time a Blue Jays batter has hit 50 home runs in a season (the highest single season total is 47 by George Bell in 1987). Bautista should at least hit 48 home runs to break the single season record.
Pretty good, right? It actually gets better. Bautista is not only hitting a lot of home runs, he is actually getting on base a ton too (he is currently tied with Daric Barton for the AL lead in walks with 73). Bautista is already only the third player in franchise history with at least 37 home runs and an on-base percentage of at least .370. If he can keep his OBP at .370 and hit just eight more home runs, he will have the most home runs by a Jays player with an OBP of at least .370.
So we can see that this season has been a very special one for Jose Bautista and the Blue Jays. Although he has not hit for a high average, he has excelled in a few key areas, such as hitting home runs, walking, and also throwing out baserunners (he is second in the MLB this year with 10 assists, only one behind Shin-Soo Choo). Hopefully he can play this well the rest of the season and for the next couple of years.
Only eight times has a player for the Blue Jays hit at least 40 home runs, and only six players have done it (Carlos Delgado did it three times, in 1999, 2000, and 2003). Bautista already has 37 home runs, and is on pace for 50, which would be the first time a Blue Jays batter has hit 50 home runs in a season (the highest single season total is 47 by George Bell in 1987). Bautista should at least hit 48 home runs to break the single season record.
Pretty good, right? It actually gets better. Bautista is not only hitting a lot of home runs, he is actually getting on base a ton too (he is currently tied with Daric Barton for the AL lead in walks with 73). Bautista is already only the third player in franchise history with at least 37 home runs and an on-base percentage of at least .370. If he can keep his OBP at .370 and hit just eight more home runs, he will have the most home runs by a Jays player with an OBP of at least .370.
So we can see that this season has been a very special one for Jose Bautista and the Blue Jays. Although he has not hit for a high average, he has excelled in a few key areas, such as hitting home runs, walking, and also throwing out baserunners (he is second in the MLB this year with 10 assists, only one behind Shin-Soo Choo). Hopefully he can play this well the rest of the season and for the next couple of years.
Monday, August 16, 2010
Park Factor at SkyDome
In a post last week, I said the following:
You can read all about ballpark adjustments and factors here. That post will show you how to calculate the ballpark factor for a park over one year or multiple years, but since the statistics have already been calculated and are already on the corresponding team and year page, you can take them from there. The numbers will all be around 100, as 100 represents a "medium" ballpark, with no advantages to batters or pitchers. If the number is above 100, the ballpark favors batters, and if it is below 100, it favors pitchers.
I have taken statistics from each year for the Blue Jays since 1990. The SkyDome opened midway through 1989, and since the statistics will be skewed for that year I decided not to include it, but since then the Jays have played all of their home games at the SkyDome. I took the ballpark factor for both multi-year and one year, the number of home runs and number of extra base hits at the SkyDome and away each year (81 games, except for 1994 and 1995 because of the strike, and 2010, which is projected), and finally the batting average and batting average on balls in play for the Jays at home and away. I have constructed a couple of graphs which I believe show that the SkyDome is in fact a fairly friendly hitter's park. (Click on any graph to enlarge it and see more detail.)
The first graph is all home statistics. I wanted to compare the park factor to the number of home runs the Jays hit at home each year. As we can see by the trendlines, the park factor and number of home runs have no correlation at all, as the park factor has stayed fairly constant (actually decreasing by about 0.2 per year), while the home runs have varied widely, with an average gain of 1.3 more home runs per year. This graph shows that the park factor calculations take into account a lot more than just home run totals.
Now I am going to look at home and away splits. Each of the following graphs shows a different statistic presented in home vs away fashion, and the goal is to get a visual representation of whether or not the SkyDome is in fact a hitter's park. Although on average, teams should hit better at home than on the road (familiarity of the stadium and its intricacies, fans cheering instead of booing, the comfort of home, etc.), the actual increase is hard to quantify, and thus, we must assume that home and away statistics should be even. We are also assuming that the road statistics are based on an average ballpark factor of 100, because we are trying to compare home vs away statistics, we want to determine the "friendliness" of the SkyDome to batters.
The first graph shows the number of home runs hit by the Blue Jays at home and on the road each season from 1990 to 2010. This graph is a pretty clear indication that the Jays hit a lot more home runs at home than on the road, especially in the last decade or so. The trendlines show that approximately 1.3 more home runs are hit at home per year, while only about 0.5 more home runs are hit on the road per year. So, we can say with good certainty that the SkyDome is more home run friendly than the average American League ballpark (since the large majority of their away games were played in the AL).
The next graph will compare the extra base hits at home and on the road. Although we already looked at home runs, it is important to look at extra base hits because we want to determine whether the balls that are not home runs on the road will either fall in the gaps for doubles or triples or whether they will be caught. If they find gaps, then the away stadium is still mostly hitter friendly, but if the balls that usually leave the park at home get caught on the road, then we know for sure that the SkyDome is much more hitter friendly for power statistics. We can see from the graphs that almost every year there are more extra base hits at home than on the road, other than a couple of isolated years in the 90s. So since there are more home runs and just as many extra base hits at home, we can safely say that the SkyDome is an above-average hitter friendly park.
The last thing I want to look at is the batting average splits. We have seen that the SkyDome is hitter friendly for power, but we also want to find out if it is hitter friendly for average. I chose to look at both batting average and batting average on balls in play, since batting average can be very inconsistent (although team batting statistics for 81 games is a fairly large sample size). Although the trendlines are almost constant in all of the cases below, we can take the average average for each statistic from them. The average batting average at home is 0.267, and on the road it is 0.264. For BABIP, at home the average is 0.297, while on the road it is 0.294. Although these are only small differences, over the course of 21 years they can become significant changes. Although we may not be able to say that hitting for average at the SkyDome is easier than on the road (see the above explanation for why hitting at home may be easier), we can say that it is at least just as easy at home as on the road.
So after taking a look at the home vs away splits for the last 21 years, I am confident that what I said last week is true. I am glad that my analysis shows that what I said was true, and that I wasn't just saying false facts. SkyDome is in fact a hitter friendly park in comparison to other American League parks, both for power and somewhat for average.
Skydome (well the Rogers Center) does seem like it's a fairly hitter-friendly park (in the early 2000s, it's "Park Factor" was over 100 consistently, which means it favored hitters, while in the last couple of years it has been below 100 a couple of times).I have thought about the statement a lot, and decided to do some more research on the topic, as I do not like making statements without valid statistical analysis backing me up.
You can read all about ballpark adjustments and factors here. That post will show you how to calculate the ballpark factor for a park over one year or multiple years, but since the statistics have already been calculated and are already on the corresponding team and year page, you can take them from there. The numbers will all be around 100, as 100 represents a "medium" ballpark, with no advantages to batters or pitchers. If the number is above 100, the ballpark favors batters, and if it is below 100, it favors pitchers.
I have taken statistics from each year for the Blue Jays since 1990. The SkyDome opened midway through 1989, and since the statistics will be skewed for that year I decided not to include it, but since then the Jays have played all of their home games at the SkyDome. I took the ballpark factor for both multi-year and one year, the number of home runs and number of extra base hits at the SkyDome and away each year (81 games, except for 1994 and 1995 because of the strike, and 2010, which is projected), and finally the batting average and batting average on balls in play for the Jays at home and away. I have constructed a couple of graphs which I believe show that the SkyDome is in fact a fairly friendly hitter's park. (Click on any graph to enlarge it and see more detail.)
The first graph is all home statistics. I wanted to compare the park factor to the number of home runs the Jays hit at home each year. As we can see by the trendlines, the park factor and number of home runs have no correlation at all, as the park factor has stayed fairly constant (actually decreasing by about 0.2 per year), while the home runs have varied widely, with an average gain of 1.3 more home runs per year. This graph shows that the park factor calculations take into account a lot more than just home run totals.
The first graph shows the number of home runs hit by the Blue Jays at home and on the road each season from 1990 to 2010. This graph is a pretty clear indication that the Jays hit a lot more home runs at home than on the road, especially in the last decade or so. The trendlines show that approximately 1.3 more home runs are hit at home per year, while only about 0.5 more home runs are hit on the road per year. So, we can say with good certainty that the SkyDome is more home run friendly than the average American League ballpark (since the large majority of their away games were played in the AL).
The next graph will compare the extra base hits at home and on the road. Although we already looked at home runs, it is important to look at extra base hits because we want to determine whether the balls that are not home runs on the road will either fall in the gaps for doubles or triples or whether they will be caught. If they find gaps, then the away stadium is still mostly hitter friendly, but if the balls that usually leave the park at home get caught on the road, then we know for sure that the SkyDome is much more hitter friendly for power statistics. We can see from the graphs that almost every year there are more extra base hits at home than on the road, other than a couple of isolated years in the 90s. So since there are more home runs and just as many extra base hits at home, we can safely say that the SkyDome is an above-average hitter friendly park.
The last thing I want to look at is the batting average splits. We have seen that the SkyDome is hitter friendly for power, but we also want to find out if it is hitter friendly for average. I chose to look at both batting average and batting average on balls in play, since batting average can be very inconsistent (although team batting statistics for 81 games is a fairly large sample size). Although the trendlines are almost constant in all of the cases below, we can take the average average for each statistic from them. The average batting average at home is 0.267, and on the road it is 0.264. For BABIP, at home the average is 0.297, while on the road it is 0.294. Although these are only small differences, over the course of 21 years they can become significant changes. Although we may not be able to say that hitting for average at the SkyDome is easier than on the road (see the above explanation for why hitting at home may be easier), we can say that it is at least just as easy at home as on the road.
Friday, August 13, 2010
Fact of the Week I: Grand Slams
Every Friday, I am going to introduce a fact that is interesting to me, but may or may not be interesting to you. So this week, I am going to look at grand slams.
So far this year, the Blue Jays have only hit two grand slams. Considering that there have already been 91 grand slams hit so far this season, the fact that the Jays have only hit two is surprising, especially considering they are leading the league in home runs by a wide margin.
Perhaps even more surprising is that the Jays have already given up five grand slams. Since the average major league team should hit and give up the same amount of grand slams (about 3 per team so far), it is interesting that the Jays have given up three more than they have hit (again, especially considering their home run exploits). And from July 3 to July 18, they had actually given up 5 and not hit one, before Yunel Escobar hit his 1st home run of the season (and 1st as a Jay).
So there is the inaugural fact of the week. Grand slams are always interesting, and although so far this year there has been an interesting set of results for the Jays, the data set is simply not yet large enough to conclude that there is a definitive pattern going on. Thus, the grand slam belongs in the fact of the week.
So far this year, the Blue Jays have only hit two grand slams. Considering that there have already been 91 grand slams hit so far this season, the fact that the Jays have only hit two is surprising, especially considering they are leading the league in home runs by a wide margin.
Perhaps even more surprising is that the Jays have already given up five grand slams. Since the average major league team should hit and give up the same amount of grand slams (about 3 per team so far), it is interesting that the Jays have given up three more than they have hit (again, especially considering their home run exploits). And from July 3 to July 18, they had actually given up 5 and not hit one, before Yunel Escobar hit his 1st home run of the season (and 1st as a Jay).
So there is the inaugural fact of the week. Grand slams are always interesting, and although so far this year there has been an interesting set of results for the Jays, the data set is simply not yet large enough to conclude that there is a definitive pattern going on. Thus, the grand slam belongs in the fact of the week.
Best, Worst Performances vs the Jays
As a follow-up to my post yesterday, where I analyzed the best and worst performances by Jays' players, today I am going to analyze the best and worst performances so far this year by players against the Blue Jays. Again, I am going to use WPA as a measuring stick, as it is the most useful game-to-game statistical tool.
The best hitting against the Jays so far was Mark Teahen of the White Sox on April 12. Teahen went 3 for 5, with a single, triple, and home run, and 3 RBIs. His leadoff homerun in the top of the 9th inning tied the game at 7, and raised the probability of the White Sox winning by 33%. Then, in the 11th inning, Mark Kotsay led off with a single, and Teahen drove him in with a triple, again raising the probability of the White Sox winning by 33%. Overall, he had a WPA of 0.761, on the day, easily the highest WPA by a Jays' opponent this season.
The best pitching performances against the Jays so far can be found here. Ervin Santana's complete game win on April 18 (boy, was that a bad week for the Jays!) has been the best performance so far. In the Angels' 3-1 win, Santana only allowed 4 hits, and the only run was an Adam Lind solo home run with 2 outs in the bottom of the ninth inning. Santana never faced more than 4 hitters in an inning, and before the home run had retired 17 Jays in a row (a Lind single in the 4th was the last hit). He steadily gained WPA over the course of the game, and since the Angels were already up 3-0 and only needed one more out to win when Lind hit the home run, his WPA barely dropped. He ended up with a WPA of 0.675, pretty easily the top pitching performance against the Jays this year. Surprisingly, the performance only merited a Game Score of 81, and is only the 4th best pitching performance (measured by Game Score) this year by an Angels' pitcher.
Now that we have taken a look at the best performances (well, the worst from the Jays perspective), we can take a look at the worst performances so far this year against the Jays. This shows the worst hitting performances, and Mike Sweeney's 0 for 5 performance on May 19 leads the way. He was already 0 for 3 with a strikeout when he came to the plate in the bottom of the 7th in a 3-2 game (Jays leading) with runners on second and third and 2 out, and when he popped out to second the Jays probability of winning increased by 13%. Then, in the bottom of the 9th, with the Jays still leading 3-2, there were runners on 1st and 2nd and two outs when Sweeney flew out to deep left, increasing the Jays probability of winning by 17% (to 100%). Those two outs were key in his WPA of -0.381 for the game. His aLI was 2.790 for the game, which means that because he made key outs at key points in the game, his WPA took a tumble.
Finally, the worst pitching performance against the Jays so far this year belongs to Bobby Jenks on May 9. Jenks entered the game in the top of the 9th inning with the White Sox winning 7-5, and did not record an out while giving up 4 hits and 4 runs (3 earned). The inning went like this: ground-rule double, single, home run, single, and that was it for Jenks as Scott Linebrink replaced him. The 3-run home run by Fred Lewis single-handedly increased the Jays winning probability by 51%, from 31% to 82%. Overall, Jenks had a WPA of -0.767, and the Jays ended up winning the game 9-7. Interestingly, Jonathon Papelbon's disaster of a ninth inning yesterday finished second on the list of worst pitching performances with a WPA of -0.739. I will be doing a post later this week about last night's game and blown saves.
So there are your best and worst performances by a Jays' opponent so far in 2010. The best performances seemed to occur mostly in April, while the worst performances happened in May (which probably have a correlation to the Jays 12-12 record in April and 19-10 record in May). Surprisingly, none of the games discussed yesterday were repeats today, but some of them were lower down on the list of good and bad performances.
The best hitting against the Jays so far was Mark Teahen of the White Sox on April 12. Teahen went 3 for 5, with a single, triple, and home run, and 3 RBIs. His leadoff homerun in the top of the 9th inning tied the game at 7, and raised the probability of the White Sox winning by 33%. Then, in the 11th inning, Mark Kotsay led off with a single, and Teahen drove him in with a triple, again raising the probability of the White Sox winning by 33%. Overall, he had a WPA of 0.761, on the day, easily the highest WPA by a Jays' opponent this season.
The best pitching performances against the Jays so far can be found here. Ervin Santana's complete game win on April 18 (boy, was that a bad week for the Jays!) has been the best performance so far. In the Angels' 3-1 win, Santana only allowed 4 hits, and the only run was an Adam Lind solo home run with 2 outs in the bottom of the ninth inning. Santana never faced more than 4 hitters in an inning, and before the home run had retired 17 Jays in a row (a Lind single in the 4th was the last hit). He steadily gained WPA over the course of the game, and since the Angels were already up 3-0 and only needed one more out to win when Lind hit the home run, his WPA barely dropped. He ended up with a WPA of 0.675, pretty easily the top pitching performance against the Jays this year. Surprisingly, the performance only merited a Game Score of 81, and is only the 4th best pitching performance (measured by Game Score) this year by an Angels' pitcher.
Now that we have taken a look at the best performances (well, the worst from the Jays perspective), we can take a look at the worst performances so far this year against the Jays. This shows the worst hitting performances, and Mike Sweeney's 0 for 5 performance on May 19 leads the way. He was already 0 for 3 with a strikeout when he came to the plate in the bottom of the 7th in a 3-2 game (Jays leading) with runners on second and third and 2 out, and when he popped out to second the Jays probability of winning increased by 13%. Then, in the bottom of the 9th, with the Jays still leading 3-2, there were runners on 1st and 2nd and two outs when Sweeney flew out to deep left, increasing the Jays probability of winning by 17% (to 100%). Those two outs were key in his WPA of -0.381 for the game. His aLI was 2.790 for the game, which means that because he made key outs at key points in the game, his WPA took a tumble.
Finally, the worst pitching performance against the Jays so far this year belongs to Bobby Jenks on May 9. Jenks entered the game in the top of the 9th inning with the White Sox winning 7-5, and did not record an out while giving up 4 hits and 4 runs (3 earned). The inning went like this: ground-rule double, single, home run, single, and that was it for Jenks as Scott Linebrink replaced him. The 3-run home run by Fred Lewis single-handedly increased the Jays winning probability by 51%, from 31% to 82%. Overall, Jenks had a WPA of -0.767, and the Jays ended up winning the game 9-7. Interestingly, Jonathon Papelbon's disaster of a ninth inning yesterday finished second on the list of worst pitching performances with a WPA of -0.739. I will be doing a post later this week about last night's game and blown saves.
So there are your best and worst performances by a Jays' opponent so far in 2010. The best performances seemed to occur mostly in April, while the worst performances happened in May (which probably have a correlation to the Jays 12-12 record in April and 19-10 record in May). Surprisingly, none of the games discussed yesterday were repeats today, but some of them were lower down on the list of good and bad performances.
Thursday, August 12, 2010
Best, Worst Jays Performances so far in 2010
I thought it would be interesting to do a quick study on the best and worst batting and pitching performances so far this year. A quick way to measure the value of a performance is WPA, which is Win Probability Added*. It sums the value of each play made by a certain player in a game. For a quick example, say that Vernon Wells comes up in an inning with the Jays chance of winning at 45% (e.g. down by one in the middle innings). If Wells hits a home run, and the chance of winning increases to 50%, then the value of the home run is 5%. If you add up (or subtract) the total changes in chances of winning the games, you will get WPA. It is a little confusing at first, but it is an extremely useful (if not the most useful) statistic for measuring a player's contribution to winning individual games or his value over a season or career.
*Quick note: I am using the WPA from Baseball-Reference, which may differ from that of Bill James or FanGraphs as each site has a slightly different WPA formula, but all numbers end up very close
The first thing we are going to look at is the best performances for Jays hitters. This shows the the most valuable game so far this year by a Jays hitter was Adam Lind against the Cleveland Indians on May 5. The Jays won the game 5-4, as Lind hit a 2-run home run with 2 outs in the 9th inning and the Jays down by 1 run. For the game, he went 2 for 4 with a walk, a run, and 2 RBIs. FanGraphs shows that the home run had a WPA value of .724, as the probability of the Jays winning the game went from 8% to 80.5%. That game by Adam Lind, and especially the home run, is the most valuable hitting performance by any Jays player so far this year.
The next thing to look at is the best performance by a Jays pitcher. Any guesses on what the result might be? Yep, it was Brandon Morrow's gem on Sunday against the Rays that had the highest WPA, which corresponds to what I described here this week. One of the biggest reasons why it was so high was because of aLI, or the Average Leverage Index. aLI measures the average pressure that a player will face in each situation, with 1 being average pressure, under 1 being low pressure, and above 1 being high pressure. (An interesting sidenote is that the top 12 best pitching performances are from starters, while we will see that 6 of the 7 worst pitching performances are from relievers. This has a lot to do with aLI, as the best performances are usually when pitchers pitch 8 or 9 shutout innings in a close ball game, while the worst performances are usually when relievers enter a close game and get lit up.) Morrow had an aLI of 1.454, which was also higher than any of the other performances on the list. A big reason why it was so high was because the Jays were only winning 1-0 throughout the entire game, so Morrow could not take any innings off because of a big lead, but had to pitch hard the entire game.
Now, the flip side. We have looked at the best performances of this year so far, now we need to look at the worst. First up, the worst hitting performances so far. Alex Gonzalez's June 23 game against St. Louis comes in as the worst performance so far with a WPA of -0.376. Gonzalez went 0 for 3 with a walk, and 2 double plays grounded into. Both double plays came after leadoff singles by Vernon Wells (in the 4th and 9th innings), and since the Jays ended up with a 1-0 loss, any run would have helped a lot. The 9th inning double play was the play with the largest change in WPA for the Jays, as the Jays went from having a 34.2% chance of winning to only a 4.9% chance of winning (WPA of -0.294). That play, along with his hitless night, gives him the top spot in the worst hitting performance of this year.
Finally, we are going to look at the worst pitching performance of this year. I don't know about you, but when I think about bad pitching so far this year, one name pops into my head: Kevin Gregg. Gregg has gotten a lot of saves (25 so far), but has also blown a couple (4 so far, resulting in an 86% save percentage). But to me, it always seems as if he pitches well under the least pressure, that is, if he comes into the 9th with a 3-run lead against the Orioles, he will always get the save, but if he comes into the 9th with a 1-run lead on the Yankees, he will blow it. And when we take a look at the leaderboard, Gregg's name is all over it. He actually holds the top 3 spots for worst pitching performances, but the one that takes the cake is his performance against Seattle on May 20. He came in with a 3-1 lead, and then: single, single, walk, walk, sac fly, single, game over. Gregg's line: 0.1 IP, 3 H, 2 BB, 3 ER, BS, and L. He threw 25 pitches, only 12 for strikes, and his WPA was an astoundingly bad -0.905, as the Jays had over a 90% chance of winning when he entered, and ended up losing.
There are your best and worst performances so far in 2010. An interesting note is that all performances came in 1-run games, with the games winning 5-4 and 1-0, and losing 1-0 and 4-3 in the respective order of the performances. This again has to do with aLI. The higher the aLI, the higher the probability that a single play (or a couple plays in a row) can swing the entire outcome of the game. And although we measured everything in terms of WPA, I believe that the results we have come up with should pass the "gut-check"; that is, if you were to eyeball some of the best and worst performances so far this year, many of these performances would be at the very top (or very bottom).
We will check back on this post at the end of the year, to find out if the results will still be the best and worst performances after 162 games. Also, tomorrow I will be working on a post similar to this one, for the best and worst performances by opposing players when they are facing the Jays. I would not be surprised if many of the same games we looked at today will be involved tomorrow.
*Quick note: I am using the WPA from Baseball-Reference, which may differ from that of Bill James or FanGraphs as each site has a slightly different WPA formula, but all numbers end up very close
The first thing we are going to look at is the best performances for Jays hitters. This shows the the most valuable game so far this year by a Jays hitter was Adam Lind against the Cleveland Indians on May 5. The Jays won the game 5-4, as Lind hit a 2-run home run with 2 outs in the 9th inning and the Jays down by 1 run. For the game, he went 2 for 4 with a walk, a run, and 2 RBIs. FanGraphs shows that the home run had a WPA value of .724, as the probability of the Jays winning the game went from 8% to 80.5%. That game by Adam Lind, and especially the home run, is the most valuable hitting performance by any Jays player so far this year.
The next thing to look at is the best performance by a Jays pitcher. Any guesses on what the result might be? Yep, it was Brandon Morrow's gem on Sunday against the Rays that had the highest WPA, which corresponds to what I described here this week. One of the biggest reasons why it was so high was because of aLI, or the Average Leverage Index. aLI measures the average pressure that a player will face in each situation, with 1 being average pressure, under 1 being low pressure, and above 1 being high pressure. (An interesting sidenote is that the top 12 best pitching performances are from starters, while we will see that 6 of the 7 worst pitching performances are from relievers. This has a lot to do with aLI, as the best performances are usually when pitchers pitch 8 or 9 shutout innings in a close ball game, while the worst performances are usually when relievers enter a close game and get lit up.) Morrow had an aLI of 1.454, which was also higher than any of the other performances on the list. A big reason why it was so high was because the Jays were only winning 1-0 throughout the entire game, so Morrow could not take any innings off because of a big lead, but had to pitch hard the entire game.
Now, the flip side. We have looked at the best performances of this year so far, now we need to look at the worst. First up, the worst hitting performances so far. Alex Gonzalez's June 23 game against St. Louis comes in as the worst performance so far with a WPA of -0.376. Gonzalez went 0 for 3 with a walk, and 2 double plays grounded into. Both double plays came after leadoff singles by Vernon Wells (in the 4th and 9th innings), and since the Jays ended up with a 1-0 loss, any run would have helped a lot. The 9th inning double play was the play with the largest change in WPA for the Jays, as the Jays went from having a 34.2% chance of winning to only a 4.9% chance of winning (WPA of -0.294). That play, along with his hitless night, gives him the top spot in the worst hitting performance of this year.
Finally, we are going to look at the worst pitching performance of this year. I don't know about you, but when I think about bad pitching so far this year, one name pops into my head: Kevin Gregg. Gregg has gotten a lot of saves (25 so far), but has also blown a couple (4 so far, resulting in an 86% save percentage). But to me, it always seems as if he pitches well under the least pressure, that is, if he comes into the 9th with a 3-run lead against the Orioles, he will always get the save, but if he comes into the 9th with a 1-run lead on the Yankees, he will blow it. And when we take a look at the leaderboard, Gregg's name is all over it. He actually holds the top 3 spots for worst pitching performances, but the one that takes the cake is his performance against Seattle on May 20. He came in with a 3-1 lead, and then: single, single, walk, walk, sac fly, single, game over. Gregg's line: 0.1 IP, 3 H, 2 BB, 3 ER, BS, and L. He threw 25 pitches, only 12 for strikes, and his WPA was an astoundingly bad -0.905, as the Jays had over a 90% chance of winning when he entered, and ended up losing.
There are your best and worst performances so far in 2010. An interesting note is that all performances came in 1-run games, with the games winning 5-4 and 1-0, and losing 1-0 and 4-3 in the respective order of the performances. This again has to do with aLI. The higher the aLI, the higher the probability that a single play (or a couple plays in a row) can swing the entire outcome of the game. And although we measured everything in terms of WPA, I believe that the results we have come up with should pass the "gut-check"; that is, if you were to eyeball some of the best and worst performances so far this year, many of these performances would be at the very top (or very bottom).
We will check back on this post at the end of the year, to find out if the results will still be the best and worst performances after 162 games. Also, tomorrow I will be working on a post similar to this one, for the best and worst performances by opposing players when they are facing the Jays. I would not be surprised if many of the same games we looked at today will be involved tomorrow.
Cycles and Games oh so Close
In this morning's post, I talked a little bit about players in their MLB debuts hitting for the cycle (which has never been done). This led me to researching about Blue Jays hitting for the cycle. I remember back in 2001 when Jeff Frye did it (interestingly enough, future Blue Jay Frank Catalanotto was on the Rangers when Frye hit for the cycle against them, and we'll talk a little more about him later), so I knew it had at least been done before, but I was interested in digging a little deeper. This shows the number of times a Jay has hit for the cycle, which has happened twice, with Frye and Kelly Gruber back in 1989.
Considering that there have been 111 cycles since 1977 (the year the Jays joined the MLB), you would think that they would have hit more than 2 (the average major league team hit 3.7 cycles over that span). Now obviously some parks are easier to hit cycles in than others (I always think of Fenway Park, with the Green Monster for home runs and doubles and the "triangle" out in right-center field for triples), but Skydome (well the Rogers Center) does seem like it's a fairly hitter-friendly park (in the early 2000s, it's "Park Factor" was over 100 consistently, which means it favored hitters, while in the last couple of years it has been below 100 a couple of times). So I wondered, how close have the Jays been to hitting cycles over the years?
The first, and most obvious thing to look at, is the number of times a Jays hitter has been only a triple away from the cycle. It happened on Saturday, in JP Arencibia's debut (which I talked about earlier today), when he had a single, a double, and two home runs (Edwin Encarnacion also fell just a triple short of the cycle on Saturday). This actually happens fairly frequently, probably even more often than you would think, as there have actually been 257 occasions where a Jays hitter has fallen just a triple short of the cycle, including 11 times already this year. On average, a Jays hitter has fallen short of the cycle by a triple 7.56 times per year.
Since triples are so rare, it is to be expected that most of the time a player misses a cycle will be because of the triple. The next most difficult hit to get is the home run, and this shows that Blue Jays hitters have missed the cycle by a home run 73 times (2.15 times per year). It last happened on June 16, when Fred Lewis had 4 hits, including two singles, a double, and a triple, but couldn't quite complete the cycle. Whereas players usually miss the triple because they aren't quite fast enough, players who miss by a home run are players who aren't power hitters.
On the contrary, those who miss the cycle by only a double are usually players who just don't get enough at-bats or just can't quite stretch a single into a double, especially if they end up with 4 hits or more. This shows that 23 Blue Jays over the years (0.68 per year) have missed the cycle by just a double. In every case but one (Ed Sprague with two home runs in 1996), players end up with either 3 hits: single, triple, home run, or 4 hits: 2 singles, triple, home run.
The most painful non-cycle occurs when a player hits a double, triple, and home run, but misses by just a single. These are rare occurrences, and as this shows, it has only happened 13 times in Blue Jays history. In all 13 games, the player only ended up with 3 hits, all for extra bases. The last time it happened was to Frank Catalanotto in 2005, who actually missed the cycle by a single in 2005, a triple twice in 2003, and a home run in 2003 and 2004. The most recent player who has missed the cycle by every single type of hit has been Vernon Wells, who missed it by a single in 2003, a double in 2002, a triple in 2002, four times in 2003, and once in 2006, 2007, 2008, and 2010, and a home run twice in 2002, and once in 2003 and 2009.
So in total there have been 368 games in Jays history (an average of 10.82 a year) where a player was one hit away from the cycle, yet only twice did the player actually achieve the cycle - only 0.54% of the time! It seems as though the Jays franchise is due for another cycle soon, and already this year there have been 12 instances where a player is one hit shy of the cycle, which is the highest amount since 2006, when there were 16 instances where a player was one hit away from the cycle (this year is on pace for 18, which would be the highest total since 2002).
I believe that we will see another cycle for the Jays by the end of 2011. The way that they have been hitting the ball the last couple of years, and especially this year, I think that it may happen before the end of this year but more likely sometime next year. They have been extremely unlucky so far in their quest for cycles (though not as unlucky as the Marlins and Padres!), and at some point the law of averages must balance out.
Considering that there have been 111 cycles since 1977 (the year the Jays joined the MLB), you would think that they would have hit more than 2 (the average major league team hit 3.7 cycles over that span). Now obviously some parks are easier to hit cycles in than others (I always think of Fenway Park, with the Green Monster for home runs and doubles and the "triangle" out in right-center field for triples), but Skydome (well the Rogers Center) does seem like it's a fairly hitter-friendly park (in the early 2000s, it's "Park Factor" was over 100 consistently, which means it favored hitters, while in the last couple of years it has been below 100 a couple of times). So I wondered, how close have the Jays been to hitting cycles over the years?
The first, and most obvious thing to look at, is the number of times a Jays hitter has been only a triple away from the cycle. It happened on Saturday, in JP Arencibia's debut (which I talked about earlier today), when he had a single, a double, and two home runs (Edwin Encarnacion also fell just a triple short of the cycle on Saturday). This actually happens fairly frequently, probably even more often than you would think, as there have actually been 257 occasions where a Jays hitter has fallen just a triple short of the cycle, including 11 times already this year. On average, a Jays hitter has fallen short of the cycle by a triple 7.56 times per year.
Since triples are so rare, it is to be expected that most of the time a player misses a cycle will be because of the triple. The next most difficult hit to get is the home run, and this shows that Blue Jays hitters have missed the cycle by a home run 73 times (2.15 times per year). It last happened on June 16, when Fred Lewis had 4 hits, including two singles, a double, and a triple, but couldn't quite complete the cycle. Whereas players usually miss the triple because they aren't quite fast enough, players who miss by a home run are players who aren't power hitters.
On the contrary, those who miss the cycle by only a double are usually players who just don't get enough at-bats or just can't quite stretch a single into a double, especially if they end up with 4 hits or more. This shows that 23 Blue Jays over the years (0.68 per year) have missed the cycle by just a double. In every case but one (Ed Sprague with two home runs in 1996), players end up with either 3 hits: single, triple, home run, or 4 hits: 2 singles, triple, home run.
The most painful non-cycle occurs when a player hits a double, triple, and home run, but misses by just a single. These are rare occurrences, and as this shows, it has only happened 13 times in Blue Jays history. In all 13 games, the player only ended up with 3 hits, all for extra bases. The last time it happened was to Frank Catalanotto in 2005, who actually missed the cycle by a single in 2005, a triple twice in 2003, and a home run in 2003 and 2004. The most recent player who has missed the cycle by every single type of hit has been Vernon Wells, who missed it by a single in 2003, a double in 2002, a triple in 2002, four times in 2003, and once in 2006, 2007, 2008, and 2010, and a home run twice in 2002, and once in 2003 and 2009.
So in total there have been 368 games in Jays history (an average of 10.82 a year) where a player was one hit away from the cycle, yet only twice did the player actually achieve the cycle - only 0.54% of the time! It seems as though the Jays franchise is due for another cycle soon, and already this year there have been 12 instances where a player is one hit shy of the cycle, which is the highest amount since 2006, when there were 16 instances where a player was one hit away from the cycle (this year is on pace for 18, which would be the highest total since 2002).
I believe that we will see another cycle for the Jays by the end of 2011. The way that they have been hitting the ball the last couple of years, and especially this year, I think that it may happen before the end of this year but more likely sometime next year. They have been extremely unlucky so far in their quest for cycles (though not as unlucky as the Marlins and Padres!), and at some point the law of averages must balance out.
JP Arencibia and MLB Debuts
A couple of weeks ago, Alex Rodriguez fouled a pitch straight back, which hit John Buck in the finger, causing Buck to exit the game. He was later placed on the disabled list, which led to the call up of top Jays catching prospect JP Arencibia. Arencibia was drafted 21st overall by the Jays in the 2007 MLB draft, and last Saturday he finally started his first major league game. He hit a home run on the first pitch of his first major league at-bat, then proceeded to get a double, single, and another home run. He finished the game 4 for 5 with two home runs, 3 runs, and 3 RBIs. The Jays won the game 17-11, and his debut was immediately praised everywhere, from Jays nation to leading off Sportscenter. But just how good was his debut?
To start off, Arencibia became the first Blue Jay to have at least 4 hits in his MLB debut. He also became only the 15th player in MLB history with at least four hits. He was only the second player since 1998 to get 4 hits (Wilson Ramos, now of the Nationals, got 4 hits in his debut for the Twins in May), and had the most RBIs for any player with at least 4 hits.
But not only did Arencibia get 4 hits, he also hit for power, with 3 out of the 4 hits going for extra bases. He became only the 4th player in major league history with 2 HRs in his debut, and the first since 1999 (and only the 2nd since 1964). Out of those players with 2 HRs (Mark Quinn in 1999, Bert Campaneris in 1964, and Bob Nieman in 1951), he was the only player with 4 total hits (the other 3 only had 3 hits). So he hit with power as well as any other player ever making their debut, but hit for average better than any player with power.
Related to the number of home runs he hit is the total number of bases he hit for. He actually had the most total bases ever hit in a major league debut (11), and became only the second player in history with at least 10 TB in his major league debut, after Mark Quinn in 1999. Finally, he became only the 8th player to be one hit shy of the cycle, and the 6th to be a triple shy of the cycle (2 other players were a home run short of the cycle).
To wrap up, in all major league debuts he became the first Blue Jay and 15th player ever to get at least 4 hits, first Blue Jay and only 4th player ever to hit at least 2 HRs, and had the most total bases ever in a major league debut. I believe that without question it was the best debut for any Blue Jay hitter ever, as well as either the best or at least top 5 best debuts for any hitter ever. So when you were watching the game, or watching the highlights afterward, you may have believed that what he was doing was special, but the statistics show just how special his debut really was.
To start off, Arencibia became the first Blue Jay to have at least 4 hits in his MLB debut. He also became only the 15th player in MLB history with at least four hits. He was only the second player since 1998 to get 4 hits (Wilson Ramos, now of the Nationals, got 4 hits in his debut for the Twins in May), and had the most RBIs for any player with at least 4 hits.
But not only did Arencibia get 4 hits, he also hit for power, with 3 out of the 4 hits going for extra bases. He became only the 4th player in major league history with 2 HRs in his debut, and the first since 1999 (and only the 2nd since 1964). Out of those players with 2 HRs (Mark Quinn in 1999, Bert Campaneris in 1964, and Bob Nieman in 1951), he was the only player with 4 total hits (the other 3 only had 3 hits). So he hit with power as well as any other player ever making their debut, but hit for average better than any player with power.
Related to the number of home runs he hit is the total number of bases he hit for. He actually had the most total bases ever hit in a major league debut (11), and became only the second player in history with at least 10 TB in his major league debut, after Mark Quinn in 1999. Finally, he became only the 8th player to be one hit shy of the cycle, and the 6th to be a triple shy of the cycle (2 other players were a home run short of the cycle).
To wrap up, in all major league debuts he became the first Blue Jay and 15th player ever to get at least 4 hits, first Blue Jay and only 4th player ever to hit at least 2 HRs, and had the most total bases ever in a major league debut. I believe that without question it was the best debut for any Blue Jay hitter ever, as well as either the best or at least top 5 best debuts for any hitter ever. So when you were watching the game, or watching the highlights afterward, you may have believed that what he was doing was special, but the statistics show just how special his debut really was.
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