The good news is that OSU baseball had its most successful season under coach Greg Beals. The Buckeyes qualified for the NCAA Tournament and won a Big Ten title of any kind (tournament or regular season) for the first time since 2009 and won the Big Ten Tournament for the first time since 2007. Six Buckeyes were drafted, the most since 1998.
That’s the good news, and it is legitimately good news. The bad news is they finished third in their NCAA regional, knocked out by an in-state foe with no particular history of being a quality baseball program (Wright State); the six drafted players are part of a mass exodus of talent that will leave the 2017 team with little returning production; and the relative success of the season should solidify Beals’ position.
I am not the type of fan that roots for my team to lose because I think that they would be better off if the coach was fired, or other similar reasoning (given the perverse incentives created by pro sports drafts, it may some times be in the team’s best interest to lose, but that never applies to college sports). So the last bit of bad news is worth it given a new trophy for the case. Nevertheless, it remains a cost associated with the season.
At first, it did not appear as if this would be the best season of Beals’ tenure. While OSU embarked on Big Ten play with a 12-6-1 record, it was not particularly impressive given the quality of competition. OSU took two out of three at home from Northwestern in the first Big Ten series, needing a come-from-behind walkoff homer to pull the series out on Sunday (Northwestern would finish twelfth in the Big Ten at 7-17). The Bucks were then swept at Maryland, won a home series against Rutgers and lost a series at Illinois to sit at 5-7 halfway through the Big Ten schedule.
Both road series featured memorable losses. In the second game against Maryland, John Havird took a no-hitter into the ninth, but was pulled with a 1-0 leading and 98 pitches after plunking the leadoff hitter. Maryland pushed across the run without the benefit of a hit, giving OSU a combined nine no-hit innings, but two hits lead to a Terrapin run in the tenth and a 2-1 loss. The opener against Illinois wound up as a 1-0 loss in fifteen innings on a wild pitch, in a game in which Illini starter (and future Baltimore first rounder) Cody Sedlock was allowed to make 132 pitches in 10 2/3 innings (one of Beals’ redeeming qualities as a coach is that he rarely pushes the envelope with his pitchers--Tanner Tully was done after 9 and 106 pitches).
The Buckeyes opened the second half with a sweep of hapless (2-22) Purdue, then won a home series from Iowa and swept Michigan at home to get to 13-8, technically still alive in the conference race heading into the finale at Minnesota (first place at 15-5). The Buckeyes won two out of three, finishing 1.5 games behind the Gophers and 1 behind Nebraska, tied for third with Indiana. At this point it could be noted that OSU had a favorable Big Ten schedule; only three of the thirteen teams finished under .500 in conference play, and OSU played all of them (Purdue, Northwestern, Rutgers), while not playing Nebraska or Indiana.
The Buckeyes opened the tournament with a win over Michigan, but melted down in the ninth inning against Iowa by coughing up a 4-0 lead before losing in ten. Their next contest with Michigan was delayed by weather, forcing the Buckeyes to beat both Michigan and Michigan State on Saturday to stay alive. On Sunday morning, the Buckeyes beat MSU again to claw back through the loser’s bracket, into the winner-take-all final against Iowa that afternoon. The Bucks led 7-4 going into the eighth, but Iowa scored three to tie it. This time, OSU would answer with a run in the ninth and hold on for an 8-7 win to cap a remarkable two days of baseball with a long overdue championship.
OSU was made a #2 seed of the NCAA Tournament, but was deemed one of the weaker #2s and placed in a bracket with #2 national seed Louisville, #3 seed Wright State, and #4 seed Western Michigan. In a recurring tournament theme of losing leads, the Buckeyes opened up 5-0 on the Raiders through three before allowing two in the fourth and four in the fifth. WSU’s 6-5 lead held until the Buckeyes rallied for two in the ninth and a 7-6 win. Alas, Louisville’s offense proved too potent, burying the Bucks 15-3, and WSU extracted revenge with a 7-3 win that ended OSU’s season at 44-20-1.
OSU lead the Big Ten (all games) in W% at .688, was a close third in EW% at .676 (Minnesota led with .685), and was second in PW% (.679 to Minnesota’s .728). OSU had a balanced team, finishing third with 5.83 R/G and fourth with 3.98 RA/G (although it should be noted that Bill Davis Stadium appears to be a solid pitcher’s park). OSU was not a strong fielding team, with a .951 mFA compared to a conference average of .946, and more importantly a below-average DER (.675 to .681 average).
OSU’s offense was power-driven, leading the conference with 57 homers and a .151 ISO and a slightly above-average .348 OBA. Beals rode junior catcher Jalen Washington hard; Washington appeared in 63 of 65 games and created 4.3 RG. Junior Jacob Bosiokovic bounced back from injuries as the primary first baseman, creating 5.9 RG on the strength of power (11 homers, .213 ISO) rather than on base skills (.335 OBA). He was drafted by Colorado in the 19th round. Troy Kuhn settled back in at second base, but had his worst offensive season of three as a starter as a senior (just 4.3 RG). L Grant Davis struggled mightily at the plate (.194/.257/.256 in 151 PA), requiring OSU to sacrifice defense for offense at the keystone.
Nick Sergakis was third on OSU with 13 homers and was second on the team with 8.8 RG and +26 RAA with a .332/.420/.542 line, enough to be a 23rd selection by the Mets (Sergakis played three years at OSU after transferring from this year’s national champions, Coastal Carolina). Shortstop Craig Nennig, who Beals’ refusal to pinch-hit for was a running frustration of mine, made that less of an issue by turning in the best offensive season of his career, although .256/.313/.366 on the strength of five longballs hardly erased any concerns about production.
The outfield was led by junior Ronnie Dawson, who while productive in his first two years really played up to his potential with a team-high 13 homer, 9.3 RG, +32 RAA, .331/.415/.611 season that netted him several All-America honors and a second-round selection by Houston. Classmate Troy Montgomery had a fine season in center, hitting .297/.420/.466 for 7.7 RG and +20 RAA and was drafted in the 8th round by the Angels. Right field was filled by a combination of Bosiokovic and sophomore Tre’ Gantt, who couldn’t match the production from his freshman season (.255/.311/.314 for 3.6 RG in 158 PA).
Freshman Brady Cherry started the year on fire at DH with five homers, but settled into a .218/.307/.411 performance over 143. Senior Ryan Leffel (.205/.283/.301 in 96 PA) took some of his playing time as the year went on. Zach Ratcliff hit better than them early (.268/.362/.341 in 52 PA), but an injury ultimately resulted in a medical redshirt.
The pitching staff was anchored by junior Tanner Tully, who continued to be the perfect image of a finesse lefty with a 3.18 RA, 3.31 eRA, 1.9 W/9, 6.5 K/9, +19 RAA over 108 innings season. Tully was picked by Cleveland in the 26th round. Senior John Havird made it two lefties at the top of the rotation, pitching solidly (4.50 RA and +2 RAA, but 3.91 eRA and 5.8 K/1.5 W in 94 innings). Sophomore Adam Niemeyer was the #3, with similar results to Havird (4.56 RA for +1 RAA, 4.37 eRA, 8.9 K/1.5 W in 71 innings). As you can see, the strength of OSU’s starters was avoiding free passes. Freshman Ryan Feltner was the most frequent mid-week starter (11 starts out of 20 appearances) and wasn’t bad for a freshman (4.72 RA for 0 RAA, 5.67 eRA, 8.0 K/3.9 W in 69 innings). Senior transfer lefty Dalton Mosbarger got a few mid-week, limited inning starts (5 of 13 appearances) and was effective (3.31 RA for +5 RAA, 4.11 eRA, 8.3 K/4.1 W in 33 innings, in addition to his 35 PA as a reserve outfielder). Sophomore transfer Austin Woody rounded out the starters (3 of 19 appearances, 6.92 RA for -10 RAA, 7.75 eRA, 6.7 K/4.2 W in 39 innings).
OSU’s bullpen was terrific, with four key relievers chipping in. Sophomore Yianni Pavlopoulos was closer out of the gate despite just nine career innings and a medical redshirt in 2015, and recorded 14 saves and solid performance numbers (3.03 RA, 3.72 eRA, 10.3 K/3.3 W in 30 innings). Sidearm sophomore Seth Kinker was the workhorse, making 38 appearances and pitching great (1.98 RA for +17 RAA, 3.27 eRA, 7.4 K/1.6 W over 55 innings). Senior Michael Horejsei’s LOOGY role expanded a little, but not too extensively, working 31 innings in 34 appearances with a 3.19 RA, and excellent 1.93 eRA and 11.3 K/2.6 W). His selection in the 21st round by the White Sox as a situational reliever capped a remarkable career arc for a pitcher who began his college career at a regional campus (OSU-Mansfield) and had just 26 career innings entering 2016. Finally, sophomore Kyle Michalik chipped in +9 RA with a 2.25 RA, 2.29 eRA, 5.1 K/1.4 W performance over 32 innings, but was definitely fourth on Beals’ bullpen pecking order. Other pitchers who saw action include junior Joe Stoll and freshman Conor Curlis, who will presumably compete for the LOOGY role next year.
While 2016 was a measured success, the outlook for 2017 is murkier. OSU had a total of 84 offensive RAA contributed by individuals--83 of those contributed by non-returning players (1B Bosikovic, 3B Sergakis, LF Dawson, CF Montgomery). Additionally, OSU must replace Kuhn at second, Nennig at short (who was a very good fielder, at least to this observer’s eyes), and Leffel at utility/DH. It will essentially be a new offense and it will lack any proven plus performers. The rotation will lose two of its top three, but profiles as a more likely strength with Niemeyer and Feltner as two obvious members. To the extent that college bullpens can be predicted, OSU is well-positioned for 2017.
However, my concerns regarding player development were only slightly allayed, and Beals’ tactics may have been toned down a little bit (even he could see that he had power and should probably give away fewer outs on the bases), but one season does not a program make. It’s a positive step, at least, which is more than can be said for most of the previous seasons of Beals’ tenure. And that’s the good news.
Monday, July 11, 2016
The good news is that OSU baseball had its most successful season under coach Greg Beals. The Buckeyes qualified for the NCAA Tournament and won a Big Ten title of any kind (tournament or regular season) for the first time since 2009 and won the Big Ten Tournament for the first time since 2007. Six Buckeyes were drafted, the most since 1998.
Thursday, July 07, 2016
Monday, June 20, 2016
I have a love-hate relationship with Cleveland.
I was born and raised in the exurbs. I left for college but eventually came back to the suburbs but only because I had to, not because that’s where I wanted to live. When it comes to sports, I always rooted for the Cleveland teams, but in all honesty have never been a diehard Cavs fan, that description being much more apt for how I follow the Indians and the Browns. Even in those cases it might not fully apply, since I pride myself on striving to be too rational about baseball to fall into the sheer emotion of fandom (post-childhood), and the Browns have been too bad for too long to not laugh at rather than lament the losses. My sheer sports fan emotions, assigning good v. evil to every game and opponent, living and dying with the team was transferred to my future alma mater around adolescence and will never be directed elsewhere.
Yet there's no question that my baseball team is the Indians, my football team is the Browns, and my basketball team is the Cavaliers. At times this has been embarrassing. Not due to the failure to win championships, but more due to the Cleveland fan culture. Cleveland fans have taken pride in their victimhood, with the heartbreaks (sometimes more real than imagined) a perverse source of pride. Where else does a 35 year distant divisional playoff game (i.e. two games removed from the championship) have a name that every young Browns fan learns ("Red Right 88")? Cleveland had some bad breaks, but more often than not they just had bad teams. Bad management, a little bad luck, and on the rare occasions when the teams had a chance to win it all, the dice rolls were not kind. But the only way one can reasonably expect to win championships is to put multiple championship-caliber teams on the field and let the chips fall where they may. Cleveland's three largely failed in that regard.
Of course, franchise ineptitude is largely not the fault of the fanbase, but there's an important distinction to be made between losing because you're bad and losing because fate didn't look kindly upon you on a given day. Cleveland fans too often conflated the two, resulting in a fatalistic feedback loop that took the former as evidence of the latter.
The other maddening element of the sports culture is the unique grip that the Browns have on the city. For all of the elation that the Cavs victory has brought, it will pale into comparison to the day the Browns win or even make a Super Bowl. The Browns still rule the landscape, and benefit from a remarkable double standard. The Indians, a franchise that has achieved more in any one of nine seasons in the last twenty-five than the Browns have in any, are struggling with attendance. There is an overwhelming cynicism towards the Indians, rooted in a lack of understanding of the economics and nature of baseball. Every trade of a free agent to be furthers the downward spiral of the relationship between the city and the team, even as those trades bring back the future objects of lament (e.g. as Bartolo Colon becomes Cliff Lee becomes Carlos Carrasco). This is not to absolve the Indians of their very real failures in drafting/international signings that only now appear to be reversing, but the Indians have run rings around the Browns and yet it is the former that I will be pleasantly surprised to see take the field in Cleveland rather than Montreal or Portland or San Antonio in, say, 2030.
Cleveland fans have also had the opportunity to root for a winner, but many have passed it up, and I cannot feel too sorry for them. In the last twenty years, OSU has won two national titles in football and been to three Final Fours in basketball. A college team might always belong to the students and alumni most dearly, but the surely the flagship state university is as much an available rooting interest as a private entity that can be moved to Baltimore at the owner's whim.
Sports irrationality aside, one thing I will say for Cleveland and Northeast Ohio is that there is a real pride in their hometown among people here. I'm not well-traveled enough to declare that this feeling is unique, but I can contrast it to my other hometown, Columbus. People in Columbus don't generally exhibit the same pride in their city that Clevelanders do in theirs. Columbus residents might be proud of OSU or proud of Ohio, but they aren't as proud of Columbus per se. Some of this may be due to sports teams; minus the recent (and so far unsuccessful) addition of the Blue Jackets, Columbus' sporting identity ties to OSU and thus more to the state than the city.
This is why it is so appropriate that Cleveland's title drought was ended about as single-handedly as one could be by one man, LeBron James. LeBron was a native son, and that meant something here. Everyone probably feels some sort of connection to LeBrown, however tenuous or forced. Mine is that LeBron and I are the same age. LeBron was a first-name basis celebrity by the time we were freshmen in high school. The night of the 2003 NBA Draft Lottery, I was in a cabin at a state park in Southeast Ohio on our mandatory "senior trip" watching as the Cavs came up with the #1 pick (and a parent chaperone insisted that they had to draft Carmelo Anthony).
LeBron was asked to shoulder the burden of the city himself, and unfair ask for anyone but especially for a rookie. And when he dragged a ragtag bunch to the team's first ever Finals appearance, he simultaneously hurt both his "legacy" in the ridiculous media environment when the Cavs were swept and obliterated any remaining thought of patiently building a worthy team around him. For the next three seasons the Cavs chased in vein, leaving him with the impossible choice of staying to try to drag this motley crew to the promised land, or going to chase titles with a group of stars.
Of course, the way “the Decision” went down was an extra gut punch, but while many Cleveland fans condemned LeBron, I’d like to think that I was fairly level-headed (this comment on The Book Blog is as intemperate towards LeBron as I got, and I still didn't lose sight of who the real villain in sports is):
The real villain in this whole thing, IMO, is ESPN. They cannot try to pass themselves off as a news outlet of any sort when they are willing to whore themselves out for an hour as a player’s personal press corps.
It has been apparent for years that ESPN wants to be part of the stories it reports on, but it has never been more plain to see then it was last night.
On another note, I do not support the childness of the Cleveland fans, but it is worth noting where they are coming from. Cleveland has not won a major sports title since 1964 despite fielding three teams (at least since 1970)--and that one isn’t even celebrated by anyone outside of Cleveland because of the NFL’s whitewash of its pre-Super Bowl history.
Yet here, nearly miraculously, was a local player who just so happened to be the best basketball prospect in anyone’s memory. He was not called the Chosen One for no reason. He was the one who was destined to finally break through the wall, to give Cleveland its championship. Twice the team has looked liked the NBA’s best team in the regular season, only to fold in the playoffs.
I’m not saying that those expectations and hopes were fair, that they all should have been thrown on LeBron’s shoulder. They plainly weren’t. Still, I may be a little biased, but I think that all things considered, this has to be one of the biggest kicks in the gut that an athlete leaving a team as a free agent has ever delivered. That doesn’t excuse Dan Gilbert or Cavs fans, but this is not just ARod leaving the Mariners.
The truth of the matter is that I was still a LeBron fan. The most endearing quality of LeBron in a sports sense to me was his support of OSU. He was on that bandwagon prior to the 2002 national championship (an article on his senior season of high school included an account of his enthusiastic celebration of this victory with his teammates), and he remained a friend of the program even after going to Miami, which he didn’t have to do. Sure, OSU is an important college asset of Nike, but Nike has contracts with a million other colleges, there was no need to keep up appearances.
It seemed like a crazy pipe dream in the spring of 2014, but was realistic by summer, and then remarkably came true. LeBron was coming back, to try to lead a new supporting cast, rebuilt largely through the fruits of the lottery picks that never would have come had he stayed (Kyrie Irving, Tristan Thompson, Dion Waters, Kevin Love via trade). For all of the fury that surrounded The Decision, had LeBron’s goal all along been to win a title in Cleveland, he couldn’t have done any better.
However, nothing is assured, and being Cleveland the natural insecurities were ratcheted up a few levels. Had the moment for LeBron passed, was he just far enough past his prime that he could not deliver? Was the supporting cast good enough (or in the case of Irving, healthy enough) to provide him support? Unexpectedly a new question emerged--would the suddenly dominant Warriors serve an insurmountable foil?
Hopefully the answers to those questions will reduce some of the irrationality of Cleveland sports observers (of course, the probability of the Cavs winning when down 3-1 was greater than the probability of a collective of sports fans becoming more rationally). Whatever small change to the city’s sports mindset might result, Cleveland’s overall inferiority complex is not going to change. On the very morning after the Cavs won the world championship, a new banner appeared on the side of a building bragging that the first traffic light was installed in Cleveland in 1914. This will certainly make the political hacks in town next month to do political hack-y things real impressed. Cleveland is a weird place.
And yet I can watch a ridiculously hokey, wildly overproduced local news commercial from 1995 and find it tugging at my inner childhood Indians partisan in a manner I can’t rationally describe. "Give me a reason for believing in Cleveland." Cleveland can surprise you.
Wednesday, May 25, 2016
Wednesday, May 18, 2016
Note: The following is a rare (for this blog) timely book review.
The premise of The Only Rule Is It Has to Work is that respected sabermetrically-inclined authors and podcasters Ben Lindbergh (Baseball Prospectus, Grantland, Five Thirty-Eight) and Sam Miller (Orange County Register, Baseball Prospectus) were given the opportunity to act as the baseball operations department throughout the 2015 campaign of the Sonoma Stompers, member of the four-team Pacific Association, a low-level indy circuit in northern California. Lindbergh and Miller were granted wide berth to put their mark on player acquisition, roster construction, and in-game strategy, and also attempt to bring modern data collection tools (PITCHf/x, video scouting, etc.) to the bush leagues.
Lindbergh and Miller are embedded deep within the team--in the front office, the clubhouse, the dugout, and even (for a moment at least) kangaroo court. Thus it serves as one of the most revealing examinations of daily life in baseball from an outsiders' perspective. Most books that have provided similar access to the inner workings of a team have been written by insiders, even if they might not fully fit into the world in which they have spent many years (think your Jim Boutons). While the life of an indy-league player is certainly less lavish than that of a big leaguer and perhaps less structured than that of an affiliated minor leaguer, it's hard to imagine that the basic human impulses of (largely) twentysomething, athletically-gifted ballplayers varies much between Sonoma and San Jose, San Jose and San Francisco. The authors are able to observe the scene with some combination of bemusement, paternal-ish concern, and comradery to give the audience a different perspective on the people who play the game. Certainly the majority of the audience members can better relate to the authors' stations in life and can now imagine how they might fit in (or not) if thrust into the life of a ballclub.
While it should hardly be necessary at this point for sabermetricians to defend themselves against scurrilous charges of not watching the games, one thing that the authors don’t reflect too closely upon but that is obvious to the reader is just how much low-level baseball they watch over the course of the summer, and just how devoted to their cause they are. Granted, Lindbergh and Miller are aided by a small network of volunteer scouts that earns the derisive nickname "The Corduroy Crew" around the league, but one or the other personally does advance scouting of nearly every game the Stompers' opponents play. This in addition to the hours spent researching potential players with their proverbial noses buried in a spreadsheet. While it would be wrong to hold Lindbergh and Miller's labors (which of course were performed with at least the secondary intent of providing fodder for a book) up as a pure representation of baseball love to be extrapolated to all of their sabermetric compatriots, it would be less wrong to do so than to brandish the common stereotype.
One of the disappointments of the project is that many of the radical ideas the authors dreamed about being able to test are never put into play. While shifts and flexible usage of the relief ace take hold in the second half of Sonoma's season, batting orders largely remain tethered to convention, starting pitchers still generally work in rotation, and the manager holds on to ultimate in-game command. While this may be disappointing to the reader longing for sabermetric red meat, the implications raise questions worth considering. Is it necessary for change in baseball tactics to come one easily digestible piece at a time? Why can a grizzled bench veteran and former pennant-winning manager of a major league team (Clint Hurdle) pivot to the approach his superiors' desire with more aplomb than a 37-year old pot-smoking player-manager who goes by Feh and dabbles in 9/11 conspiracy theories? Do the high stakes of the majors actually make them a more suitable laboratory for experimentation, as players and managers can count on their million dollar checks regardless of whether they may look unconventional on the field? While these questions can't be answered by the book, it provides some entertaining anecdotal evidence to consider.
Along the way, the Stompers inadvertently break ground in the social realm of baseball as well, as one of the authors' hand-picked college signees, relief ace Sean Conroy, comes out as the first openly gay player in professional baseball. The authors do an excellent job of relating this part of the story without falling into self-congratulations or allowing it to swamp the baseball portion of the narrative. Lesser authors with a less interesting baseball story to tell (and perhaps less respect for their subject) could have easily allowed Conroy's story (which includes being one of the Pacific Association's most valuable pitchers) to crowd out other aspects of the Stompers' season in the narrative, and could hardly have been blamed for it.
The authors alternate chapters, and if you are a regular listener (as I am) of their Effectively Wild podcast, you will likely be able to pick out which voice you are reading after a couple of pages even if you forget for a moment whether it is an odd or even chapter. Lindbergh's earnest verbosity and Miller's cheerful nihilism carry through to the written page in book format yet complement each other well, imbuing a diversity of style to the writing while still making you feel as if you are reading the same book.
As luck (or the residue of design) might have it, the story has a dramatic conclusion that I will not spoil here, except to say that I'm very glad the majors have resisted the allure of the half-season format, except for every ninety years when unusual circumstances take hold (if I live to see baseball in 2071 I promise to be grateful and not complain about it too much). Were it ever turned into a movie, the scriptwriter would even have something of a "pick your own adventure" opportunity to affect the outcome with only the proverbial flap of a butterfly's wing.
And maybe that's one of the lasting lessons to take away from The Only Rule Is It Has to Work. That despite the careful planning, the on-the-fly adjustments due to injuries or player poaching (at this level), the dedication of the players and support staff, the superstitious rituals, and the motivational speeches that are poured into baseball clubs, not to mention the attempts to drag baseball kicking and screaming into the sabermetric age, we will never be able to escape what seem from our imperfect perspective to be random rolls of the die.
Tuesday, May 10, 2016
Last time, I explained how we could use Linear Weight Ratio, an offensive metric developed by Tango Tiger, as a shortcut in finding a variable which I call the “component deflator” and symbolized as “a”.
Last time I focused on its application to park adjustments, but that’s not necessary. In fact, the component deflator can be applied generally to any situation in which you’d like to know what across the board percentage change in mutually exclusive offensive events would you have to see in order to alter run scoring by some scalar (assuming you are willing to accept that the linear weight values stay constant, which is certainly an assumption that must be applied with care).
So there are any number of questions that this kind of approach can address. One that I will consider in this piece is “What would a replacement-level hitter’s batting line look like?" First a few caveats, though. As Tango has pointed out, there really is no such thing as a replacement-level hitter. A replacement player is a replacement player because his overall contribution, offense and defense, is at a level so as to have no marginal value. Thinking about it in terms of a replacement level hitter only confuses the issue.
However, the analytical structure of assuming that replacement level players are average in the field, and thus calculating their value as their offensive contribution above a “replacement” performer specific to their position plus their defensive contribution above an average performer at their position can be a useful approximation. It is the structure used by a number of approaches, including Pete Palmer’s TPR (which is above average, but the same principle holds), Keith Woolner’s VORP, and the RAR figures I post here at the end of each season. I am not claiming that this approach is optimal or superior to the others, only that if applied with caution it can be a useful model of player value.
For the sake of this post, let’s just assume that we are going to use a model where a replacement level player hits at some percentage of the league average. Then, if we’d like to know what his batting line might look like, we can use the component deflator approach. The good thing is that we don’t have to worry so much about the fact that we have static linear weights, since we are now applying the process to individuals for whom we’d like to hold the weights constant (ignoring the Theoretical Team arguments). So that caveat is loosened in this application.
Of course, this approach carries some of its own caveats with it: one is that we are again developing a model in which all events are equally deflated. It might actually be that replacement level hitters tend to not be as deficient in BA as one might expect. Or maybe teams are willing to trade BA for power in a replacement level hitter. This is a specific model with specific assumptions, and it is not necessarily reality.
Anyway, if we define R as the percentage of league average (or positional average or anything else if you’d like), then we can just plug it into one of the formulas from last time, and carry out the rest of the calculations as explained in that post:
New LWR = ((LWR/s' + x)*R - x)*s'
In my RAR estimates, I assume that a replacement player’s R/O is 73% of the positional average, where the positional average is figured by taking the overall league average times a long-term offensive positional adjustment. The positional adjustments I use are (note: you can tell how long ago I wrote this by the use of 2008 league totals):
C = .89, 1B/DH = 1.19, 2B = .93, 3B = 1.01, SS = .86, LF/RF = 1.12, CF = 1.02
Combining these adjustments, the LWR component deflator procedure, and the overall 2008 MLB offensive averages, here is the offensive output expected from a replacement player at each position:
How do these numbers look to you? My impression is that the batting averages are too low; teams may resort to replacement level players at 73% of league R/O, but they may be those that trade secondary skills for BA points of equivalent value. (assuming that players of this profile even exist in reality)
Anyway, you don’t have to take any of this too seriously, and I’ve already stated that the assumptions and admitted they may not model reality, so I’m not going to spend too much time justifying the results. Instead, I have another potentially amusing if not completely realistic application.
Namely, it is to take the initial statistics of a real hitter, and maintaining the proportional relationships between his positive events, projecting what his line would look like at a different level of productivity. For example, what would a replacement level hitter with Barry Bonds’ bizarre 2004 proportional relationships look like?
In this case, I’ll assume that a replacement player would have a 3.50 RG. Bonds’ 2004 line comes out to a 18.26 RG, so our “R” will be 3.5/18.26 = 19.2%. This line results:
The bizarro Bonds would hit just .140, but would still manage to put up a .416 secondary average. Of course, such a player would never really exist, but if he did, his offensive value would be about the same as the other replacement level guys above.
Let’s look at Tony Gwynn, 1994 to see what this would look like for a very good singles-type hitter:
And we could try going the other way. What would Mario Mendoza, superstar look like? Here’s the transformation from Mendoza’ career line to a 8 RG:
In order to turn Mendoza’s no-secondary skills profile into an all-time upper echelon great, you have to allow him to hit .400, and increase all of his positive rates by 81%.
This translation approach falls squarely under the category of "toy"; please don’t get the impression that I’m elevating it to any greater pedestal.
Monday, April 25, 2016
If I tell you that a hitter has a line of .260/.330/.400, and I tell you that he plays in a park that inflates run scoring by 5%, what would you estimate his batting line would be in a neutral park? Suppose we know nothing at all about the park other than its effect on runs scored; we don’t know how it changes the rates of home runs, or strikeouts, or hits, or any other statistical category. We don’t know the dimensions or the altitude or the fence height or the type of playing surface, so we can’t make any estimates based on those factors either. How are you going to answer this?
One path you could take is to assume that the park influences the rates of all events equally. You could assume that the park will increase the number of singles by X%, the number of walks by X%, the number of home runs by X%, etc. Will this necessarily be the best approach? No; after all we expect the maximum real world park factor for walks to be much less extreme than for home runs, for instance. It may be an acceptable approximation, but it would be a stretch to say it was the optimum approach
However, what if we are only concerned about value, and don’t wish to adjust individual components? Personally, this is the question that interests me the most. I don’t care if the park benefits a certain type of hitter more than another; I just want to know what the effect was on runs scored. If a batter’s style or approach enables him to take more advantage of a park than the average batter, I don’t wish to take that credit away from him, as it generates real value for his team.
So if I want to adjust a player’s slash line for park, I don’t care about the precise park effect on doubles, walks, or strikeouts. I want to answer “What batting line would provide equivalent value in a neutral park, assuming that the proportional relationships between the components of this man’s line were a constant?” In other words, if this batter had a 2:1 ratio of singles to extra base hits in reality, I want him to still have a 2:1 ratio when we’ve adjusted his line for park.
This is where the approach mentioned above comes into play; assume the park has an equal effect on each mutually exclusive component of the batting line, and go from there. In order to start this process, we will make the assumption that the linear weight values will remain constant as we move between environments. Obviously this is a faulty assumption; linear weight values are dependent on context. However, linear weight values are also fairly stable over similar run environments, particularly in the case of the out value when we are using the -.1 type. A park increasing run scoring by 5% shouldn’t have too dramatic of an effect on the coefficients so as to render our conclusions invalid. Nonetheless, for more extreme parks, the potential for problems will be larger.
Let us define a new variable, a. a is what I will call the “component deflator” (I am borrowing the term “deflator” from Stephen Tomlinson’s “run deflator” as defined in the Big Bad Baseball Annual). Assuming stable linear weight values, using the definition of terms from the last post, and limiting the scope of our categories to the basic mutually exclusive offensive categories (singles, doubles, triples, homers, walks, batting outs) we can start by saying that:
RC/PF = new RC
New RC/Out = (sS*a + dD*a + tT*a + hHR*a + wW*a + x*(1 - S*a - D*a - T*a - HR*a - W*a))/(1 - S*a - D*a - T*a - HR*a - W*a)
All we have done is assume that the frequency of each event will be equally effected, by a factor of “a”. We can also simplify the out term to be 1 - a*(S + D + T + HR + W).
Just as seen in the last installment, we can cancel out the out terms from the numerator and the denominator, and thus save ourselves a lot of hassle, and write everything in terms of Linear Weight Ratio:
New LWR = (S*a + d'D*a + t'T*a + h'HR*a + w'W*a)/(1 - a*(S + D + T + HR + W)
Where new LWR = (New RC/O - x)*s'.
I have kept symbols everywhere to keep this as general as possible, but let’s remember what they actually are. d', t', etc. are all known values, all fixed coefficients. S, D, T, etc. are simply the frequencies of a set of mutually exclusive events. The only unknown variable in this equation is a, the common deflator.
Occasionally I find it necessary to include a disclaimer that I am not a mathematician, and this is one of those times. The way I am going to describe solving for severely overcomplicates the matter and makes the connection between LWR and a seem tenuous at best. And it’s true, you don’t need to convert to LWR in order to do this type of approximation; I just like doing in that way because of the aforementioned canceling out of the out term in the RC/O numerator and denominator.
To solve for a, let’s define the LWR numerator as N:
N = S + d'D + t'T + h'HR + w'W
One way to look at this is that, in effect, we have stated the player’s positive linear weight contributions from all events as an equivalent number of singles, since singles have a weight of one.
Let’s also write the denominator in an equivalent number of singles. Find (S + D + T + HR + W)/S and call this D (sorry for doubling up with doubles here). This the ratio of all non-out PA outcomes to singles.
Then, the New LWR can be viewed as this:
New LWR = N*a/(1 - D*S*a)
We have reduced all of the events down to an equivalent number of singles, and can solve for the ratio of singles under our new conditions to singles under old conditions that result in the desired new LWR. This is a, and it is the same ratio that will apply to the other events (except outs, which have to be handled differently):
a = New LWR/(N + D*S*New LWR)
Now, our player’s new rate of singles will be S*a. His new rate of doubles will be D*a, his new rate of home runs will be HR*a, and so on for all events except outs. His new rate of outs will be 1 - S*a - D*a - T*a - HR*a - W*a, or substitute “PA” for “1” if you are using the actual count of each event rather than the per-PA frequencies. The outs can also be adjusted as 1 - a*(1 - Outs), or PA - a*(PA - Outs), depending on whether you are using frequencies or counts.
Those of you who are astute and who are not totally bewildered by the circuitous way I defined terms and got to this point (which should eliminate most of you, since if you are in fact astute you are rightfully thinking “What the heck is wrong with this guy?”) may notice that the execution here is similar to Bill James’ “Willie Davis method”. And that it is. James converts a player’s batting line into an equivalent number of singles, finds the proportion of translated singles to original singles necessary to yield the right new number of Runs Created (which involves the quadratic formula due to the nature of the RC formula), and adjust the other events accordingly. So the procedure I’m using here is not in anyway new or unique, it is just an application of it in the case of Linear Weights.
Let me walk you through an example, since I’ve made this confusing as all get out. Let’s review the ERP-based LWR that I derived last time for example purposes:
LWR = (S + 1.67D + 2.33T + 3HR + .67W)/(1 - S - D - T - HR - W)
Let’s suppose that we want to take a league-average player from the 1990 NL and project his statistics in an extreme park, a mid-90s Coors type park with a 1.20 PF. Here are his statistics:
With some basic algebraic manipulations on the equations in the last post, we can go directly from LWR to New LWR by this formula:
New LWR = ((LWR/s' + x)*adjustment - x)*s'
Where we recall that x is the linear weight value of an out (-.097 in this case), s' is the reciprocal of the linear weight value of a single (2.058 in this case), and adjustment is the scalar effect on runs/out (1.20 in this case). So:
New LWR = ((.545/2.058 - .097)*1.2 + .097)*2.058 = .614
From here, we need to find “N” and “D”:
N = S + 1.67D + 2.33T + 3HR + .67W = .167 + 1.67(.041) + 2.33(.006) + 3(.021) + .67(.086) = .370
D = (S + D + T + HR + W)/S = (.167 + .041 + .006 + .021 + .086)/.167 = 1.922
And now we can solve for a:
a = New LWR/(N + D*S*New LWR) = .614/(.370 + 1.922*.167*.614) = 1.083
In order to increase this player’s RC/O by 20%, we need to increase his singles, doubles, triples, homers, and walks by 8.3% each. This yields a new batting line of:
So this player has gone from hitting .257/.321/.384 to hitting .281/.348/.419. His park-adjusted value has been held constant, as have his relative frequencies of each positive PA outcome. The key is that he has more of all the positive events, and thus less outs.
In case you are curious, from the limited set of frequencies defined here, BA is (S + D + T + HR)/(1 - W); OBA is S + D + T + HR + W; and SLG is (S + 2D + 3T + 4HR)/(1 - W)
Monday, April 11, 2016
Note: The series of four posts I will be posting over the next month were written a long time ago, apparently in 2009. Since I have not been prolific in producing new material lately, I figured I might as well post some older stuff I’ve written that at the time I didn’t deem good or interesting enough to post. I did not vet all of the material in them, so any inaccuracies are my fault but do not necessarily reflect my current thinking.
Linear Weights Ratio (LWR) is an offensive metric developed by Tango Tiger, based on Linear Weights. Since it was developed and explained by Tango, there is really no need for me to step in and write a post that may just serve to confuse you. And I have not defined everything in exactly the same way he did, which will only add to the confusion.
I have always liked to write descriptions of other people’s research, for a couple of reasons. One is as a sort of critique/peer review, which does not have to be critical--it can also point out the positives about an approach. A second is so that if I use something later (and I have an upcoming post that uses LWR in the vein of Bill James' "Willie Davis method"), my readers can have some degree of confidence that I understand the topic at hand. All too often you will see people use metrics that they don’t really understand. By writing about the ones I’m using, I will be presenting you with sufficient evidence to draw your own conclusions as to whether or not I understand the tools I am using.
Let’s begin by focusing only on the basic, mutually exclusive offensive events: singles, doubles, triples, home runs, walks, and batting outs (AB - H). For now, we will assume that those categories encompass every possible outcome of a plate appearance. Let us also assume that we have some set of linear weights which give the value of each of those events: s is the value of a single, d of a double, t of a triple, h of a home run, w of a walk, and x of an out. Additionally, I am approaching this problem with absolute (total runs scored) weights, so x is something like -.1, not -.3. Tango’s LWR used the -.3 type value.
Given those assumptions, we can of course write:
RC = sS + dD + tT + hHR + wW + xO
Let’s consider “S”, “D”, etc. to be per PA frequencies (again, these events are assumed to encompass all possible PA outcomes, so PA = S + D + T + HR + W + O). If that is the case, we can rewrite O as 1 - S - D - T - HR - W, and write an expression for RC/Out:
RC/O = (sS + dD + tT + hHR + wW + x(1 - S - D - T - HR - W))/(1 - S - D - T - HR - W)
The out term can be canceled out, leaving us with:
RC/O = (sS + dD + tT + hHR + wW)/(1 - S - D - T - HR - W) + x
You can see that there is no need for the out term to be included at all; we are still implicitly including outs, but we don’t need to include them in the equation. The numerator of the expression is the run contribution of each event, excluding outs, while the denominator is outs. This is what I will call rLWR, for run LWR:
rLWR = (sS + dD + tT + hHR + wW)/(1 - S - D - T - HR - W)
In figuring his Linear Weight Ratio, Tango adds an additional wrinkle, and sets the weight of a single equal to 1, with the other weights changing proportionally. We can define s' as 1/s, and use that to define d' = d*s', t' = t*s', etc., and write LWR as:
LWR = (S + d'*D + t'*T + h'*HR + w'*W)/(1 - S - D - T - HR - W)
At this point I’ll plug in some actual numbers from the basic ERP equation I use ((TB + .8H + W - .3AB)*.324). This is not an optimal equation, and that’s okay because my point here is not to present a formula that you should use, just to demonstrate how you can derive your own formula for LWR based on whatever set of linear weights you are using. When that ERP equation is expanded, it becomes:
ERP = .486S + .810D + 1.134T + 1.458HR + .324W - .097(AB - H)
Which yields s’ = 2.058 and the following LWR equation:
LWR = (S + 1.67D + 2.33T + 3HR + .67W)/(1 - S - D - T - HR - W)
If you are using the actual counts of each event rather than the per PA frequencies, this could be written the same except PA would replace 1 in the denominator.
It is easy to convert between LWR and R/O, and it is a linear process. The equations are:
R/O = rLWR + x
rLWR = R/O - x
R/O = LWR/s' + x
LWR = (R/O - x)*s'
What alterations do we have to make to include non-batting outs in our ratio? This can be tricky since we can no longer assume a uniform value for outs across types. But we just need to ensure that the above relationships still hold, and weight the event in the numerator accordingly. (LWR*s' + x)*Outs must equal RC. We can expand that out:
(LWR numerator/Outs*s' + x)*Outs = RC
which simplifies to:
LWR numerator*s' + x*Outs = RC
For any specific event, x is known (the -.097 value), Outs is known (each out is worth one out), s' is known (2.06 in this case), and the RC weight of the event in question in known (let’s say we have CS at an overall value of -.3), so all we need to do is solve for the needed coefficient in the LWR numerator:
(RC weight - x)/s' = LWR numerator
For the CS example:
(-.3 - (-.097))/2.06 ~ = -.1