Tuesday, October 21, 2014

An Exciting World Series

I hate this World Series. I hate the Giants who are trying to win their third championship in five years even though almost the exact same team was unable to make the playoffs in those other two years. I hate any sentence that involves the phrase “even number year.” I hate that the Giants’ best hitter is their catcher. Not that Buster Posey isn’t amazing, but any team that can’t get at least a better AVG or SLG from any other position should be ashamed of itself. I hate how even with Matt Cain injured and Tim Lincecum far past the point of being a viable starter that the Giants were able to still find enough quality starts to make it to the postseason.

As much as I hate the Giants though, it’s nothing compared to how much I despise the Royals. Sure, this all started because I didn’t want the Blue Jays to have the longest postseason drought in the league, but with every unlikely walkoff win I find myself hating this team a little more. Simply put, this team has no business being here, and they’re stealing an opportunity from a much more deserving team.

David Schoenfield has explained how this is basically the worst World Series matchup of all time. The Royals were 9th in runs scored, 4th in runs against, and had only the 7th best win-expectancy in the American League. I wish I could attribute their overachievement to effective managing, but 8 playoff games have been more than enough to disprove that. For as pathetic they’ve been as a team though, individually they’ve been even worse.

No player on the Royals hit 20 home runs. Only 3 players even managed to hit 10 balls out of the park. No American League pennant winner has done even one of those since the 1959 White Sox (excluding shortened seasons). In fact only the 1982 Cardinals have won the pennant without players hitting for such power since the mound was lowered in 1969.

Highest individual RBI total on the Royals: Alex Gordon’s 74. Last AL pennant winner to not have a player reach 75 RBIs in a non-shortened season: The 1916 Boston Red Sox. That includes a lot of teams playing 154-game seasons.

The Royals don’t even walk. Alex Gordon led the team with 65 walks. Only two teams (2010 Rangers, 1990 Reds) were able to win a pennant without hitting that threshold since the mound was lowered. Alex Gordon also led the team with a .432 slugging percentage. Another feat unaccomplished by an AL pennant winner since the mound was lowered, and occurred only once in the NL (1973 Mets).

If there are no batting stars, maybe at least there are some pitchers to attract people to watch! Wins may be an outdated stat but the Royals are only the second AL pennant winner (2008 Rays) to not have a 15-game winner since the mound was lowered. They do have that amazing bullpen though, and if the game reaches the 7th inning before I fall asleep I’ll be excited to watch them.

I really wanted to turn this into a referendum on the current playoff format, unfortunately that’s tough to do. The Giants wouldn’t have made the playoffs under the previous system, but the Royals would have been the traditional Wild Card team. Expanding the first round to seven games wouldn’t have even done much as the Royals would have been up 3-0 going into the fourth game of the ALDS at home against the Angels.

Instead I’ll write about what it means to have an exciting playoffs. The games this year have been close, competitive, with frequent extra-inning affairs and walkoff hits. Many of them from unlikely sources such as Kolten Wong or Travis Ishikawa. The underdog mentality, focused on both the players and the teams, seems to bring fans to their feet and cheer. But is this really what we should be celebrating during the playoffs?

MLB plays a 162-game season, precisely because the game-to-game variability in baseball requires such a large sample size before the good and bad teams can be differentiated. Unfortunately the playoffs present the exact opposite scenario. A one game series, followed by a best-of-five series, followed by two best-of-seven series, with a ton of offdays squeezed in don’t truly capture the seasonal flow of baseball. That can make them exciting because it creates a situation where anyone can win. The problem is that when anyone can win, anyone can win. This year is a prime example.

Baseball is exciting already. There’s no need to manufacture situations to create more excitement. When that happens we end up with undeserving winners. The underdog story is fun, but it leaves a sour taste in your mouth. In the future when we look back at the narrative and storylines of the 2014 season, at no point until the final pages will we ever think read either the Royals or Giants are the best team. That isn’t a fun twist ending, it’s a bait-and-switch.

I don’t think that we need to go back to having only 1 or 2 teams make the playoffs in each league. Different schedules and injuries aren’t necessarily well reflected in the standings. The playoff structure could use revisions though to ensure that the best teams are given their earned advantages. I’m not against the excitement of an underdog, but I do find it more exciting watching the best teams face off against each other.

Then again, if the Royals are going to win 8 straight games none of this matters anyway. 

Wednesday, October 1, 2014

Examining Playoff Probabilities

On June 6, 2014, the Toronto Blue Jays record stood at 38-24. They had a 6 game lead in the division, had just won 15 of 17 games, and were the only team in the AL East with a positive run differential (+53). According to the playoff odds on the mlb.com standings page, powered by Baseball Prospectus, the Blue Jays had an 86% chance of making the playoffs.

Maybe it was the cynical Jays fan in me who had been sitting witness to a 20-year playoff drought, but I couldn’t help look at those playoff odds with a high degree of skepticism. The Jays had just finished an epic winning streak, everything Encarnacion swung at was going out of the park, the team was still relatively healthy, and the rotation consisted of Stroman who had two career starts, Hutchinson coming off a bad injury with no real track record, and JA Happ who is JA Happ. Everything to that point in the season that could have gone right had gone right. Buehrle was even 10-1 and if there’s one thing that Buehrle has shown over his career is that his final numbers rarely change.

The factor that scared me the most though was that there were still 100 games left in the season. We’ve seen enough division winners lose 6 game leads in September in recent years, so I’m to believe that there’s only a 14% chance that the Jays get passed with almost 4 months left? Forget the Jays, it seemed crazy to me that any team could have such high playoff odds with so many games left to play.

So with the season now over, it seems prudent to review the playoff probabilities and how they compared to the actual resulting playoff teams. First let’s quickly review how Baseball Prospectus determines the playoff odds.

Firstly, a type of adjusted Pythagorean winning percentage is determined for each team. This is based on three factors: 1) Pythagorean record based on runs scored and allowed, 2) Expected record based on normalized runs scored and allowed, and 3) Expected record based on normalized runs scored and allowed adjusted to strength of opposition. This record is then regressed toward the mean.

Each game is then “simulated” one million times based on the calculated winning percentages for each team. For each game the winning percentage of each team is given a random adjustment to account for day-to-day variations, as well as a slight adjustment depending on whether it is the home or away team. The expected winning percentage for a game is determined using the log5 method. For each simulated game, a uniform random number between 0 and 1 is chosen. If the number is less than the winning probability for Team A, then Team A wins the game, if it is greater, than Team B wins the game. After each of the one million simulations, the number of times that a team makes the playoffs is divided by one million to determine the playoff probability. The simulations are repeated each day for all future games. Further details are available here and here.

I gathered all the probabilities for every team for every day for the past three seasons. Although this isn’t a completely valid assumption, I considered the probabilities calculated for each day as an independent event. This resulted in 16935 samples. I then grouped the samples into 5% bins and determined how many of the samples corresponded to teams that made the playoffs. If the calculated probabilities are correct we would expect the percentage of teams in each bin that made the playoffs to equal the percentage value corresponding to that bin. The results are shown in the following graph, with the expected height of each bar equal to the black line.

The results actually fared better than I expected, with a 6.5% root mean squared error (RMSE). My suspicions were correct that the model tended to be overconfident, in that not as many teams in the 80% range make the playoffs as predicted and similarly more teams in the 20% range do make the playoffs than predicted, however not to the extreme degree that I would have thought.

It’s helpful to also examine different parts of the season. The results for the first half of the season are shown in the following figure. This again shows that the model is overconfident and would benefit from pushing more teams toward the 50% mark. However it is still quite accurate given how many games are left to play and the vast changes that occur with so many games remaining. The RMSE is only slightly higher than for the full season results, at 7.2%.

The results for the second half of the season are actually worse, 8.6% RMSE. This is surprising since fewer games need to be simulated so there is less left up to chance. However that also leaves less room for error and less time for teams to regress to the mean. There were seemingly a few teams that the model should have been more confident in making the playoffs in the 70% range.

It should be noted that this is still a fairly limited sample. The 16935 samples are really only 90 teams, of which only 30 made the playoffs. However I’m still fairly impressed by the performance of the model, especially given that it does not make any predictions about team activity. The same winning percentage is used as the base for calculations (before the random variation is applied) for every remaining game in the season. This means the effects of the pitching rotation are ignored. The Mariners would effectively have the same odds of winning any game that Felix Hernandez starts as they would in any game that Roenis Elias starts. This assumption is difficult to avoid since rotations can be extremely difficult to predict more than five days in advance. However the random variation applied to each game could become more structured to possibly account for this.

Similarly injury concerns or future trades aren’t factored into the model. Neither is the chance that a team out of the running will use most of its 40 man roster in September and drop its expected winning percentage further. The expected run values can also be difficult to calculate after all the offseason activity, and for that reason the playoff odds are kept constant for the first month of the season.

After analyzing the odds though, I’ll probably take them a little more seriously next season, at least before I can perform another analysis with an added year of samples. After all large division leads early in the season still mean something. On that same June 6th, the Giants had a 9 game division lead and a 98% playoff probability. They may have lost the division lead, but they still clinched a wild card berth with 3 games left to play.

Wednesday, July 9, 2014

Serving Your Time

AP Photo/Newly

According to the MLB CBA, players with at least 10 years of major league service time who have been with their current club for at least 5 consecutive years cannot be traded without their consent. This clause is rarely relevant due to either its difficulty to obtain or the fact that the player has already negotiated a no-trade clause into his contract. Most players never accumulate 10 years of service time and reaching that point while staying with a team for at least five straight years usually requires a significant skill level. This is why these rights are so rarely mentioned. The caliber of player who would have them would usually be able to negotiate a no-trade clause into their deal regardless. Derek Jeter can't be traded because of his 10-and-5 rights, but if those didn't exist I hardly think he'd have a difficult time obtaining a no-trade clause.

Many players also are able to get a no-trade clause before they even have 10-and-5 rights. This is usually the result of being drafted by a team and signing an extension before hitting free agency. Adam Wainwright has only slightly over 8 years of service time, but when he signed his extension in 2013 he also got a full no-trade clause. This effectively only matters until the end of the 2015 when his 10-and-5 rights would kick in about a month into the season, however it does mean that if he chooses to waive his rights and be traded he would keep the no-trade clause on his new team.

There are less obvious cases though where a player, not necessarily a superstar, is able to stick around with a team for long enough to get his 10-and-5 rights. These are particularly important when there was no no-trade clause previously negotiated into his contract. The following are some of the more interesting cases where a player could gain 10-and-5 rights over the next year and what effects it may have. Players are listed with their service time entering the 2014 season in the form years.days. Note that a year of service time is equal to 172 days.

Jose Bautista (8.165 ST, with Blue Jays since middle of 2008 season)
Jose Bautista will gain his 10-and-5 rights a week into the 2015 season. He's signed through the 2015 season with a team option for 2016 which will be his age 35 season. At the moment that 2016 option seems like a no-brainer to pick up, but with the Jays aging and expensive core, that 2016 option may become difficult to exercise if Bautista cannot be traded without his consent. If the team and Bautista do not age well over the next season, only warm memories may keep him in Toronto in 2016.

John Lackey (11.095 ST, with Red Sox since 2010)
John Lackey has an extremely unusual contract. Due to injury concerns when he signed with the Red Sox, there was a clause inserted into his contract that if Lackey missed significant time due to injury, the Red Sox get a team option for 2015 at $500K. With Lackey having missed the 2012 season, but now back to pitching adequately, that option for next season looks like a steal. However since it comes after his initial 5 year deal with the club finishes, Lackey will also get his 10-and-5 rights if the Red Sox pick up the option. It's ironic that one of the most tradeable contracts in baseball becomes untradeable due to the length of Lackey's previous deal. I don't think that this would prevent the Red Sox from picking up the option, however if they try to negotiate an extension instead, a no-trade clause is not something that Lackey would need to negotiate.

Coco Crisp (10.158 ST, with Athletics since 2010)
Now in his fifth season with Oakland, Crisp has already signed three different contracts with the Athletics. This is one of the more unusual scenarios for 10-and-5 rights to pop up. A player bounces around with a number of teams (the A's are Crisp's fourth), however a number of short deals allows a player to stay in one city long enough to gain his no-trade rights. With Crisp signing an extension through 2016 (plus a 2017 vesting option), if he stays with the Athletics for the rest of the season, he can no longer be traded without his consent. This doesn't exactly fit in with the Athletics M.O. as he'd be the only player on the team with a no-trade clause and the Athletics are one of the more active teams on the trade market. It therefore wouldn't shock me if Crisp was dealt before the trade deadline this year just so the Athletics can avoid this situation. They may be a first place team right now that relies on Crisp's skills, but it's small moves like this that allow small market teams like Oakland to be competitive every year without being hamstrung by difficult contracts.

Brandon Phillips (9.022 ST, with Reds since 2006)
There have been trade rumours surrounding Phillips for a couple years now and to protect himself he had a limited no-trade clause (can't be dealt to 10 teams) negotiated into his most recent deal. However once we pass the August trade deadline this year, that limited no-trade clause will turn into a full no-trade clause under his 10-and-5 rights. That's a scary proposition for the Reds who have Phillips signed through 2017, his age 36 season. Middle infielders aren't exactly known for aging gracefully: his OBP and SLG are both down for the third straight year. This will be a tough decision for the Reds who are still trying to make the playoffs this season, but if they keep Phillips past the trade deadline, he's their second baseman for the next three years.

Rickie Weeks (8.131 ST, all with the Brewers)
Rickie Weeks has spent his entire career playing second base for the Brewers where he was once an all-star in 2011, however has since been a poor hitter and fielder, and only part time player. He has a vesting option for 2015 based on his PAs this season that he will almost assuredly not reach. This likely means that this season will be Weeks' last in Milwaukee. He's now a below average player who would get his 10-and-5 rights a little over a month into next season. The Brewers can find comparable talent elsewhere (Scooter Gennett is working well) without being held hostage by a no-trade clause.

Jorge De La Rosa (9.015 ST, with the Rockies since 2008)
De La Rosa will gain his 10-and-5 rights before the end of the season, but as a free agent going into 2015, the Rockies shouldn't feel any more pressure to deal De La Rosa than they would if he wasn't gaining those rights. De La Rosa will probably be looking to get a multi-year deal in free agency and will explore his options around the league. It doesn't make a lot of sense for the Rockies to sign De La Rosa given their current situation especially since he would get a no-trade clause, something De La Rosa would probably have difficulty obtaining from most other teams.

Matt Belisle (8.019 ST, with the Rockies since 2009)
Belisle is a free agent after this season so he would only get his 10-and-5 rights near the end of next season provided he re-signed with the Rockies, so this is likely irrelevant, but Belisle is a particularly interesting case since he is a reliever. Staying with a team for five seasons is generally the exception for relievers, even for the best of them. No current closer for example is even close to obtaining 10-and-5 rights. Belisle hasn't exactly been exceptional for the last few seasons and I doubt that the Rockies are interested in making him one of the few relievers in the league with trade veto rights. It's seems likely that Belisle is pitching in a different jersey next season.

It should be noted that for all these cases when I say a team is "stuck" with a player due to their 10-and-5 rights that isn't entirely true. Players can waive their 10-and-5 no-trade rights just as any player would with a no-trade clause negotiated into their contract. However these are all situations where the team likely never intended for a player to get a no-trade clause and may soon have to deal with the consequences of the players' increased leverage.

Wednesday, April 9, 2014

Why I Don't Use FIP

Note: This was originally posted on Fangraphs Community Research.

Over the last decade, Fielding Independent Pitching (FIP) has become one of the main tools to evaluate pitchers. The theory behind FIP and similar Defensive Independent Pitching metrics is that ERA is subject to luck and fielder performance on balls in play and is therefore a poor tool to evaluate pitching performance. Since pitchers have little to no control over where batted balls are hit, we should instead look only to the batting outcomes that a pitcher can directly control and which no other fielder affects. In the case of FIP, those outcomes are home runs, strikeouts, walks, and hit batters.

However there are many serious issues with FIP that collectively make me question its usage and value. These issues include the theory behind the need for such a statistic, the actual parameters of the formula’s construction, and the mathematical derivation of the coefficients. Let’s address these issues individually.

Control over balls in play

A common statement when discussing FIP or BABIP is that pitchers have little to no control over the result of a ball once it is hit into play. A pitcher’s main skill is found in directly controllable outcomes where no fielder can affect the play, such as home runs, strikeouts, and walks (and HBP). In trying to estimate a pitcher’s baseline ERA, which is the objective of FIP, the approximately 70% of balls that are put into play can be ignored and we can focus only on the previously mentioned outcomes where no fielder touches the ball. 

The concept of control is a little fuzzy though and something I believe has been misappropriated. It is definitely true that the pitcher does not have 100% absolute control over where a batted ball is hit. There is no pitch that anyone can throw that can guarantee a ball is hit exactly to a particular spot. However in the same vein, the batter doesn’t have 100% absolute control either. If you were to place a dot somewhere on the field, no batter is good enough to hit that spot every time, even if hitting off a tee. 

However this lack of complete control should not in any way imply that the batter or pitcher doesn’t have any control at all over where the ball is hit. Batters hit the ball to places on the field with a certain probability distribution depending on what they are aiming for. Better batters have a tighter distribution with a more narrow range of possibilities and can more accurately hit their target. For example consider a right-handed batter attempting to hit a line drive into left field on an 80 mph fastball down the heart of the plate. A good hitter might hit that line drive hard enough for a double 30% of the time, for a single 30% of the time, directly at the left fielder 10% of the time, and accidentally hit a ground ball 20% of the time. Conversely, a worse batter who has less control over his swing may hit a double 10% of the time, a single 10% of the time, directly at the left fielder 15% of the time, an accidental ground ball 25% of the time, and in this case not even get his swing around the ball fast enough and instead hits the ball weakly towards the second baseman 40% of the time. 

Where the pitcher fits into the entire scheme is in his ability to command the ball to specific locations, with appropriate velocity and spin, as to try to sway the batter’s hit distribution to outcomes where an out is most likely. Consider the good hitter previously mentioned. He accomplished his goal fairly successfully on the meatball-type pitch. What if the same good batter was still trying to hit that line drive to left field, but the pitch instead was a 90 mph slider on the lower outside corner? On such a pitch the good batter’s hit distribution may start to resemble the bad hitter’s hit distribution more closely. This is a slightly contrived and extreme example, but it also encompasses the entire theory of pitching. Pitchers are not trying to just strike out every batter, but instead pitch into situations and to locations where the most likely outcome for a batter is an out.

By this reasoning the pitcher has a lot of control over where and how a batted ball is hit. This does not mean that even on the tougher pitch that the batter can’t still pull a hard double, or even that the weak ground ball to the second baseman won’t find a hole into right field, these are all still possibilities. However by throwing good pitches the pitcher is able to control a shift in the batter’s hit probability distribution. Similarly, better batters are able to make adjustments so that their objective changes according to the pitch. On the slider, the batter may adjust to try to go opposite field. However a good pitch would still make the opposite field attempt difficult. 

This is all to say that better pitchers have more control over how balls are hit into play. They are able to command more pitches to locations where the batter is more likely to hit into outs than if the pitch was thrown to a different location. Worse pitchers don’t have such command or control to hit those locations and balls put into play are decided more by the whims of the batter. FIP takes this control argument too far too the extreme. There is a spectrum of possibilities between absolute control over where a ball is hit and no control over where a ball is hit that involves inducing changes in the probability distribution of where a ball is hit, which is how the game of baseball is actually played. As a simple example, we see that some pitchers are consistently able to induce ground balls more frequently than others. Since about 70% of all plate appearances result in balls being put into play, it is important to actually consider this spectrum of control instead of just assuming that the game is played only at one extreme.

Formula Construction

Let’s pause though and ignore my previous argument that a pitcher can control how balls are hit and we’ll instead assume that all the fielding independence theories are true and we can predict a pitcher’s performance using only the statistics in the FIP formula. This introduces an immediate contradiction since none of the statistics used in the FIP formula (except HBP, which has the smallest contribution and is a prime example of lack of control) are in fact fielder independent. The FIP formula is not actually accounting for its intended purpose. 

The issue of innings pitched in the denominator has been addressed before. Fielders are responsible for collecting outs on balls in play which therefore determines how many innings a pitcher has pitched. However all three of the statistics in the numerator are also affected by the fielding abilities of position players, especially in relation to ballpark dimensions. Catchers’ pitch framing abilities have been shown recently to heavily affect strike and ball calls and could be worth multiple wins per season. Albeit rare events, better outfielders are able to scale the outfield fences and turn potential home runs into highlight reel catches.

More commonly though, better catchers and corner infielders and outfielders can turn potential foul balls into outs. When foul balls are turned into caught pop-ups or flyballs, the at bat ends, thus ending any opportunity for a walk or a strikeout which may have been available to a pitcher with worse fielders behind him. This is particularly harmful to a pitcher’s strikeout total. Whereas a ball landing foul only gives an additional opportunity for a batter to draw a walk, it also moves the batter one strike closer (when there are less than two strikes) to striking out. 

Similarly, instead of analyzing the effects of the fielders, we can look at the size of foul territory. Larger foul territory gives more chances for fielders to make an out since the ball remains over the field of play longer instead of going into the stands. Statistics like xFIP normalize for the size of the park by regressing the amount of flyballs given up to the league average HR/FB rate, however there is no park factor normalization for the strikeout and walk components of FIP. 

We can see the impact immediately by examining the Athletics and Padres, two teams whose home parks have an extremely large foul territory. By considering only the home statistics for pitchers who threw over 50 IP in each of the last five seasons, the Athletics pitchers collectively had a 3.25 ERA, 3.74 FIP, and 4.05 xFIP, while the Padres pitchers collectively had a 3.38 ERA, 3.84 FIP, and 3.86 xFIP. In both cases FIP and xFIP both drastically exceeded ERA. Also, of the 46 pitchers who met these conditions, only 9 pitchers had an ERA greater than their FIP and only 7 had an ERA greater than their xFIP, with 6 of those pitchers overlapping. This isn’t a coincidence. Although caught foul balls steal opportunities away from every type of batting outcome, it is more heavily biased to strikeouts since foul balls increase the strike count.


The mathematics of the FIP formula may be my biggest problem with FIP, mostly because it’s the easiest to fix and hasn’t been. I’ve seen various reasons for using the (13, 3, -2) coefficients in derivations of the FIP formula. Ratios of linear weights, baserun values, or linear regression coefficients are the most common explanations. However none of these address why the final coefficient values are integers, or why they should remain constant from year to year. 

There is absolutely no reason why the coefficients should be integers. Simplicity is a convenient excuse, but it’s highly unnecessary. No one is sitting around calculating FIP values by hand, it’s all done by computers which don’t require such simplicity. By changing the coefficients from their actual values to these integers, error and bias is unnecessarily introduced into the final results. Adjusting the additive coefficient to make league ERA equal league FIP does not solve this problem. 

The baseball climate also changes yearly. New parks are built and the talent pool changes. This changes the value of baseball outcomes with respect to one another. It’s why wOBA coefficients are recalculated annually. However for some reason FIP coefficients remain constant. The additive constant helps in equating the means of ERA and FIP but there is still error since the ratios of HR, BB, and K should also change each year (or at least over multi-year periods). 

I’ve calculated a similar version of FIP, denoted wFIP, for the 2003-2013 seasons using weighted regression on HR, (HBP+BB), K, all divided by IP as they relate to ERA. If we treat each inning pitched as an additional sample, then the variance of the FIP calculation for a pitcher is proportional to the reciprocal of the amount of innings pitched. Weighted regression typically uses the reciprocal of the variance as weights. Therefore in determining FIP coefficients we can use each pitcher’s IP as his respective weight in the regression analysis. The coefficients for the weighted regression compared to their FIP counterparts are shown in the following graph.

FIP and wFIP coefficient values

Ignoring the additive constant, since 2003 each of the three stat coefficients have varied by at least 22% from the FIP coefficient values and are all biased above the FIP integer value almost every year. In 2013 this leads to a weighted absolute average difference of 0.09 per pitcher between the wFIP and FIP values, which is about a 2.3% difference on average. However there are more extreme cases.

Consider Aroldis Chapman, who had a 2.54 ERA and 2.47 FIP in 2013. On first glance this seems to indicate a pitcher whose ERA was in line with his peripheral statistics and if anything was very slightly unlucky. However his wFIP came to 2.96. If we saw this as his FIP value we might be more inclined to believe that he was lucky and his ERA is bound to increase. This difference in opinion would come purely from use of a better regression model, without at all changing the theory behind its formulation. That is a poor reason to swing the future outlook on a player. 

However even with current FIP values, no one would draw the conclusions I did in the previous paragraph that quickly. Upon seeing the difference in FIP (or wFIP) and ERA values, one would look to additional stats such as BABIP, HR/FB rate, or strand rate to determine the cause of the difference and what may transpire in the future. This in fact may be the ultimate problem with FIP. On its own it doesn’t give us any information. Even with the most extreme differentials we always have to look to other statistics to draw any conclusions. So why don’t we make things easier and just look at those other statistics to begin with instead of trying to draw conclusions from a flawed stat with incorrect parameters?

Sunday, January 19, 2014

The Most Wonderful Time of the Year

AP Photo/Kathy Willens
I look forward to the BBWAA Hall of Fame results announcement each year like a kid on Christmas morning. It's not so much about celebrating the players who pass the threshold and are elected (although that is enjoyable), but instead about a process so insane that it's like a fiery car crash that you can't avert your gaze from. Even knowing that it's going to be a complete mess doesn't ruin it for me. My hopes are raised up super high and every year there is some new kind of nonsense I never could have expected that clears the bar. Whether it's hypocrisy, retrograde morality, conflict of interest, or spite, there are always new presents each year to unwrap.

Let's start by looking at the logjam on the current ballot and what can be expected to change next year. In 2013 the electorate voted for an average of 6.6 players per ballot. With the inclusion of Maddux, Glavine, and Thomas this year, that number jumped to 8.4 players per ballot with over 50% of voters using all 10 of their allocated ballot slots. I think it's a fair statement to say that 8.4 players per ballot is about the maximum that we're going to see. If you didn't vote for the maximum number this year, I don't see what can change that would make a significant difference next year to increase the number of players voted for. So what will be the effect next year and what does it mean for the rest of the ballot?

There are 3.43 votes per ballot falling off the ballot from this year to 2015. Of that, 2.73 votes are attributed to Maddux, Glavine, and Thomas, while the remainder can be attributed to Jack Morris and the players who failed to reach the 5% threshold. The 2015 ballot will see the additions of Randy Johnson, Pedro Martinez, and John Smoltz, all of whom have excellent Hall of Fame cases and don't have any PED rumors. They will probably collectively approximately match the 91% average that Maddux, Glavine, and Thomas got this year. Gary Sheffield will also join the ballot and probably get at least 10% of the vote. Craig Biggio will almost assuredly make the Hall of Fame next year after falling just two votes short this time around. Already a number of writers admitted to wanting to vote for him but did not see him among their top 10 candidates. I'm guessing they will change to vote for him next year just to clear him off the ballot, as will other writers to always join the bandwagon when a player seems guaranteed entry. I'll guess that he gains about 5% of the vote to finish around 80%. If we account for all these changes, it adds up to about 7.85 players per ballot and that includes what I think is a conservative estimate for Sheffield. This also doesn't include some votes which may be cast to players like Nomar Garciaparra, Carlos Delgado, or other newcomers who are unlikely to last more than one ballot. So even if some of the more stubborn writers retire from casting their votes and younger more liberal writers take their place, we're likely looking at only about 0.6 votes per ballot opening up to repeat players on the ballot (excluding Biggio). Given the multitude of worthy players on the ballot, but the divisive nature of some of there credentials, this isn't likely be enough to solve the backlog. So to players like Bagwell, Piazza, Bonds, Clemens, Raines, Schilling, and Mussina: they should expect almost the exact same thing next year as this year.

Those two missing votes for Biggio are therefore going to be a big contributor to the continuing ballot logjam, well along with insane writers and the 10-player rule. If Biggio got those two extra votes and made the Hall of Fame on this ballot, we'd see about 1.35 votes per ballot become available to the ballot holdovers. That could definitely be enough to push at least Piazza and Bagwell over the threshold. Obviously it's not that simple since a large number of Biggio voters already voted for Piazza and no one can vote for him twice, but we could at least see better progress to alleviating the 10-player limit problem.

The writers this year bring out emotions ranging from perplexed to angry. Let's start with angry.

The revisionist history that occurs this time of year is more than enough to raise blood pressures, but the problem is so widespread and expected now that someone not voting for Bonds or Clemens hardly registers a notice anymore. Unfortunately PEDs, whether admitted, suspected, or imaginary, are too often not the entire explanation for a ballot. Keeping up with his yearly regiment, Murray Chass provides a solid dose of insanity this year:
Fans of these players and even non-fan observers will ask how I can consider someone a cheat if he has never tested positive. I have two answers:
  1. Some of them might have used steroids before baseball began testing for performance-enhancing substances and stopped before the tests could catch them.
  2. If I’m wrong on any particular player, so be it, but I’d rather err on the side of caution. I wouldn’t want to learn two or three years after the fact that I had helped elect a cheater. Anyway my one vote won’t keep anyone out of the Hall.
Couldn't his first point be applied to literally every single player who has ever played the game, including his longtime favorite Jack Morris. Could explain how he maintained the strength to throw all those innings Chass loves. The second point is lunacy too. His one vote, along with one of the many others I'm about to list kept Biggio out of the Hall this year. Although nothing would tickle me more than to see Greg Maddux admit to using steroids in his Hall of Fame speech, just to see the mental gymnastics guys like Chass would have to execute to try and process it and refit the narrative.

The voters affiliated with mlb.com revealed their ballots and there are three that deserve particular attention. Ken Gurnick voted only for Jack Morris due to "more than a decade of ace performance[s]. As for those who played during the period of PED use, I won't vote for any of them." I guess we can forget for a minute that steroid and amphetamine use dates back decades before Morris even started playing, but Morris' career didn't even end until 1994. I'm not entirely sure when this "PED era" started, but I think we can probably include Jose Canseco's 1988 MVP season in it. Even if we assume that season was the start, Morris still pitched 1436.1 innings from that season on, about 38% of his career total. So if we're going to cast blanket statements across all players from an era, let's at least try and figure out what the era is.

Terence Moore of mlb.com also had an interesting ballot, consisting of Glavine, Maddux, Thomas, McGriff and Lee Smith. He says his "voting method is simple: numbers and feel. The numbers part is self-explanatory. As for the feel part, a Hall of Fame candidate needs to make you feel as if he belongs with the others in Cooperstown." Sorry Mr. Moore, when your ballot looks like that, the numbers part isn't self-explanatory.

Marty Noble voted for only Glavine, Maddux, and Morris. In a move done in what I can only believe is the assumption that he's the only voter, Noble explains "I don't want 28 people entering the Hall at once, so I limited my checks on the ballot to three. That ought to be enough to go with the three managers." Good move, forget how many people are actually worthy of induction, let's just be concerned about how many chairs we can fit beside the podium. At least this is easier than actually thinking about the players' qualifications. In the words of Noble, "angst returns next year."

As I've mentioned numerous times, I completely disagree with not voting for Bonds and Clemens, but I can at least understand the path one takes to omit their names. However it's difficult to understand voting for only one of them. If you're willing to vote for one of them, you're clearly comfortable voting for a player associated with PEDs. Once that consideration is taken, Bonds and Clemens are by far the best hitter and pitcher on the ballot by any measure. It's not even close. Yet somehow Clemens ended up receiving four more net votes than Bonds. Barry Rozner, William Center, and Richard Griffin all voted for Bonds but not Clemens, while Peter Abraham pulled the reverse.

Barry Rozner actually did the same thing last year. According to Rozner, "following the guidelines of the vote, I've left off the steroid guys." Bonds gets his vote though because "I've always said he was a 400-400 guy before he got on the stuff, so he gets my vote." Ignoring the fact that he presumes to know exactly when Bonds started juicing, couldn't this exact same argument be applied to the two halves of Clemens' career? I can't find William Center's explanation, but I am particularly curious since he voted for only 8 players, so Clemens couldn't have been excluded for lack of space.

Richard Griffin dropped Clemens from his ballot after voting for him last year. His explanation is largely a soliloquy on how the current voting method works (he talks about how once you are a Hall of Famer, you are a Hall of Famer, regardless of which year you were elected in; if this is true, then can we all agree to just vote for the worthy players the first time they appear on the ballot?). He never explicitly states why Clemens was left off his ballot, but it appears that he was dropped to make space for Morris in his final year of eligibility. If he thinks Morris is worthy of election, than I understand dropping a better player given the 15th-year situation. What I don't understand is why he dropped Clemens. If it's strictly from a talent perspective, than dropping pitchers worse than Clemens from his ballot like Maddux or Glavine seems more appropriate. I'll talk more about Peter Abraham's ballot at the end.

Luckily though, for every Richard Griffin who considers all Hall of Famers equal, we have an electorate of writers like Rick Gosselin who feel that a first-ballot vote should be reserved for only a select few. After not voting for Bonds or Clemens last year, Gosselin added them both to his ballot in this election. I'll let him summarize:
Their credentials say they belong. But first-ballot Hall of Famers are enshrinees above any doubts or suspicions. There are doubts and suspicions about both Bonds and Clemens. So they may have forfeited their right to first-ballot election, but they haven’t forfeited their right to become Hall of Famers.
Everyone who holds a vote back against Bonds or Clemens likes to point out the morality clause on the ballot instructions. I can't remember which clause it was though that specified that a first-ballot Hall of Famer was better than the rest.

Larry Rocca has a particularly interesting ballot, Morris, Nomo, Raines, Trammel, but that isn't nearly as interesting as his history and explanation. Somehow I don't think it's a coincidence that he voted for Nomo and that he spent almost five years in charge of Business Operations for the Chiba Lotte Marines in the Nippon Professional Baseball League. There are more gems in his explanation though. He takes a stand against everyone from the steroid era (with an exception for Nomo), including Maddux and Glavine for having "have had a huge standing in the game and the union" and "there is no evidence, as far as I know, that they made any serious effort [to influence the leadership to take a harder stance against steroids]. GUILTY!" I love the guilty until proven innocence assumption, not just for taking steroids, but also for taking a stand against them. Couldn't this really be applied to anyone with any association with the game though? Like a journalist like Larry Rocca? Also I guess he's just going to ignore the fact that Frank Thomas was one of the most outspoken players on steroids for his entire career.

I also don't understand the lack of consistency to some voters. Steven Marcus' ballot of Maddux, Thomas, and Morris seems extremely limited on first glance, but it's even more limited when knowing that last year Marcus voted for Bonds, Clemens, Piazza, Biggio, and Morris. It can't be about limiting the election to three people like Marty Noble is trying, and his vote last year showed he is willing to vote for PED players. Dan Le Batard was banned from voting for giving away his ballot, but I almost have to wonder if Marcus has done the same thing. He needs to explain how his ballot can change so drastically from year-to-year. 

One of the real shames of this entire process is that some of the really crazy ballots will never be known. It's a secret ballot process, so unless a writer chooses to release his ballot, the rest of the world is left guessing. For example, none of the ballots were released from the people who voted for Moises Alou, Luis Gonzalez, Eric Gagne, J.T. Snow, Armando Benitez, Jacque Jones, or Kenny Rogers. I'm sure there's some great nonsense we're missing out on there.

I mentioned above that it's difficult to understand how someone can vote for Clemens but not Bonds. Difficult, but not impossible. With that I want to end with two ballots that I don't necessarily agree with, but still highly respect the thought process they put into it. Some voters clearly tried to avoid any angst, as Marty Noble described it, but the ballots of Peter Abraham and Jayson Stark show a clear thoughtfulness and effort that should be universally applied.

The current 10-player limit on the ballot means that some voters may have to leave players off their ballot that they deem worthy of induction. This logjam can be solved in one of two ways: 1. A rule change from the Hall of Fame or BBWAA (unlikely) or 2. enough players being elected to clear enough space each year for only the worthy candidates. Peter Abraham and Jayson Stark both strategically voted according to the second solution. They recognized that just voting for the 10 players they deemed to be the best, although appropriate, would not solve the problems of the current environment. They instead took their list of candidates they found deserving of induction, and voted for the 10 among them most likely to be elected on this ballot, without leaving anyone off who might drop off the ballot entirely, to try and help clear the backlog of players. That's how Abraham ended up leaving Bonds off his ballot. He had 12 players for whom he wanted to cast votes. Trammel was left off due to his low vote total, and he flipped a coin to decide which of Bonds and Clemens would share the same fate. Although according to the numbers Bonds has a much better Hall case than Bagwell, as the results showed Bagwell is far more likely to be elected and clear space on the ballot first (and Bonds isn't falling below 5% any time soon).

I look forward to going through all this nonsense again next year, where as I outlined above, nothing is likely to change.