If you play in a traditional 12-team 5×5 roto auction league with 25-man rosters and a $200 FA budget per season, you might constantly feel like there is solid waiver-wire talent out there, but your roster is too stacked to cut anyone. So, you offer your league-mates a trade of two or three mediocre players for one of their better players, but they are facing a similar roster crunch and immediately see right through your pernicious plan. It can be tempting to cut the lowest-production, lowest-upside player on your roster, which in many cases is the $1 catcher you drafted. But is that catcher really providing value to your roster? Let’s break it down.

Let’s say you draft Realmuto this year for $10 and expect a line of 13 HR, 53 R, 58 RBI, 7 SB, .275 AVG (Steamer projected line, ~500 PA).  The other cost of drafting Realmuto is the opportunity cost of his roster spot. In a typical fantasy week, there are three or four days where your typical starting lineup is not intact. Whether it’s because a team is having an off-day or one of your regular starters is DTD with a bruised toe, holes in your lineup are bound to happen. A smart streamer can look for good matchups and plug those holes. If you have unlimited pickups allowed in your league, then there is no cost to picking up a player if you have an open roster spot. In my league, I can pick up players for $1 on free-agent days (M/W/F).

This begs the question: if you are streaming to fill in holes four times per week over 26 weeks of the regular season, and each game you plug in a streaming player you get 4 PA, then that is going to equal just over 200 PA and cost you around $78 FAAB (assuming three pickups per week * 26 weeks, and one of your streamed pickups fills holes twice in one week for a total of four fill-ins). What does a slash line of 200 PA for a waiver-wire bat look like?

Kevin Pillar screams waiver-wire bat. His Steamer projection reduced to 200 PA looks like: 5 HR, 25 R, 20 RBI, 5 SB, .270 AVG. That’s quite worse than Realmuto’s line in every way excepting AVG. It amounts to a little less than 50% of Realmuto’s line at the cost of $78 FAAB. Now you could argue that maybe amidst all your streaming you end up picking up a Jonathan Villar 2016 breakout type of bat and end up sticking with him and getting immense value, but that’s easier said than done. Maybe you are also going to research pitcher vs. batter matchups on a daily basis and you get an edge there, but that is also easier said than done.

How does the 200 PA of Kevin Pillar compare to a $1 draft day, bottom of the barrel catcher’s line? Even poor Jonathan Lucroy is projected by Steamer to beat this line: 10 HR, 44 R, 46 RBI, 2 SB, .268 AVG. Other such luminaries projected to outshine it include Tucker Barnhart, Christian Vazquez, and Tyler Flowers. Pretty much any catcher who is a starter and can bat .250+ for a season will put up much better counting stats than the Pillar line.

Long story short — even though your catcher’s line may look meek, and they don’t play every day, making your roster look thin, it will still likely be better than waiver-wire lineup hole streaming. Better to save your FAAB cash for other needs. If you play in an unlimited transaction league, you would still need about 500 PAs of Pillar to exceed the Realmuto line. That’s a lot of transactions, and you might not have time to get all the necessary PAs in. Punting C is like heeding the siren calls — it can be very tempting, but also a dangerous and costly exercise. Staying the course with the catcher you drafted is usually the best call in terms of value per FAAB dollar spent.

The Mariners recently inked Juan Nicasio to a 2-year/$17-million deal in their first significant addition to their pitching staff this offseason. After years as a middling starter, Nicasio emerged as a rock-solid relief option with the Rockies in 2014 before the Dodgers fully bought into his potential as a reliever the following year. The Pirates then acquired him and shifted him into the rotation a bit in 2016; however, he had more success in their bullpen and moved there full-time in 2017. He was again on the move last year, though — this time playing for two new teams — but he never started a game, posting a cumulative 2.61 ERA over 72.1 IP in 76 appearances.

He’s on the wrong side of 30, and breakout relievers tend to pop up and decline quickly, but it can be argued that Nicasio has done nothing but improve since moving into the bullpen.

As a reliever, Nicasio is largely a two-pitch pitcher, primarily throwing a four-seam fastball and a slider. He had occasionally mixed in a sinker and changeup in previous years, but 2017 saw Nicasio throw a four-seam fastball or slider 98.31% of the time. This pitch mix in combination with his K/9 dipping from slightly over 10 to just under 9 may raise a couple eyebrows, but Nicasio also improved his command considerably.

His 6.9% BB% in 2017 was his lowest since his debut season and marked a second straight year of improvement, and his 24.7% K% compares well to previous years. This would suggest that Nicasio is only getting more efficient with his outs, not striking guys out at a lesser rate. And sure enough, his 1.08 WHIP last year was by far the lowest it’s ever been.

A quick look at his splits from 2017 showed a distinct improvement against left-handed batters compared to previous years.

In his largest sample yet, Nicasio made huge strides.

Since improvements against opposite-handed batters tend to suggest an improvement in a pitcher’s changeup or breaking ball, and given that Nicasio essentially throws just two pitches, his slider seemed like a good starting point. I found that (per Brooks Baseball) it had an entirely different shape in 2017.

While Nicasio’s slider was laterally less impressive in 2017, it made up for that with reduced drop.

Here is his slider in 2016 with a little frisbee action.

And here it is in 2017 a bit more tightly wound.

Nicasio’s slider was devastating to right-handers in 2015 and 2016 (cumulative .218 wOBA/.221 xwOBA), but it seemingly fell into the swing path of lefties, as they smashed it for a .369 wOBA/.272 xwOBA in the same period. In 2017, lefties floundered against it for the first time, posting just a .194 wOBA/.175 xwOBA. But his other slider disappeared.

Using this somewhat cutter-like breaking ball against RHB in 2017 yielded a .302 wOBA and .320 xwOBA. Considering the fastball didn’t play up (.298 wOBA/.334 xwOBA), that kind of performance is a slight concern, but righties’ triple slash against him was still an encouraging .225/.296/.367 (.287 wOBA).

On the surface, the Mariners seem to have gotten a quality reliever at about market rate for his talent, but I think there is still some upside here. Certainly, in this new slider, Nicasio has found a legitimate weapon against LHB, but the Mariners must hope his natural slider is not lost. In order to remain a high-quality, high-leverage setup man — the kind that posts sub-3 ERAs — he’s going to have to bring out both.

Jack Morris pitched 18 seasons while Bartolo Colon has now pitched 20. They both have a career winning percentage of .577. Morris has 2478 career strikeouts while Colon has 2454. Morris had 254 wins while Colon has 240 in an era where they are harder to obtain. Colon won a Cy Young while Morris’s highest finish was third. Morris has an ERA+ of 105, compared to Colon’s career ERA+ of 107. In fact, if you only looked at their first 15 years, Colon’s ERA+ of 114 outperforms Morris’s ERA+ of 109 even more!

Perhaps you strongly believe that, despite their statistical similarities, Jack Morris was significantly better than Bartolo Colon. Still, the fact that an argument could be made that Colon is as good a pitcher as Morris shows just how big a mistake the Modern Eras Committee made in electing Jack Morris to the Baseball Hall of Fame this weekend.

The list of players who were once viewed as “obviously not Hall of Famers” does not stop at Colon, either. In his article on ESPN.com, David Schoenfield said that it would be foolish to treat Morris as a benchmark for Hall of Fame induction. This argument is in defense of the Hall of Fame’s level of “rigor” — many think that without maintaining a certain level, the Hall of Fame may lose it’s significance. However, I believe there is another characteristic that the Hall of Fame must preserve even more so than rigor in order to maintain its credibility — and that is justice. If the Hall of Fame exposes itself as being discretionary in its election of members, it will quickly lose its relevance.

By electing Jack Morris to the Baseball Hall of Fame, voters both lowered the level of rigor previously required for election and have left the Hall of Fame in a current state of injustice until the following eligible players are also elected: Curt Schilling, Mike Mussina, Andy Pettitte, Dave Stieb, Rick Reuschel, Orel Hershiser, David Cone, Sam McDowell, Luis Tiant, Kevin Brown, Vida Blue, Bret Saberhagen, and Kevin Appier,

And the following cases are re-opened for election: Dwight Gooden, David Wells, Jim Kaat, Tommy John, Wilbur Wood, Ron Guidry, Jimmy Key, Frank Tanana, Dennis Martinez, Mark Langston, Chuck Finley, Mark Buehrle, Frank Viola and Jose Rijo.

Every single one of these 30 pitchers had a higher career ERA+ than Jack Morris and have either a higher career value, a higher peak value, or both.

Looks like Colon may be able to hang up his cleats a little more confidently this off-season now that Morris is in the Hall.

Charlie Blackmon is an atypical human being.

For one thing, he is a professional baseball player, meaning he is in the extreme upper echelon of athletic ability. But he is atypical even in his personal life, and his recent success has only highlighted his eccentric personality. He still drives a 2004 Jeep Grand Cherokee that he got in high school. He once had to be rescued on the side of the highway by DJ LeMahieu when he ran out of gas. He buys his clothes from Amazon. And of course, he is easily recognized by his impressive beard-and-mullet combo (the latter of which is pronounced “mu-lay” according to Blackmon).

Based on all his quirks, it should be no surprise just how unique his major-league career has been. He didn’t see regular playing time until his age-28 season, an age when some guys are already entering free agency. Despite this late start, he has steadily grown into an MVP candidate. In 2014, his first full season, Blackmon posted 2.0 fWAR, the exact threshold for a starting caliber player. In the three subsequent seasons, he posted an fWAR of 2.3, 4.1, and 6.5. I thought it seemed rather strange to have back-to-back seasons with ~2 WAR improvement, so I went to the leaderboards.

I searched for all batters with a minimum of 400 PAs in each of the past three seasons, producing a sample of 111 players. Then, I calculated the difference between each player’s 2015 WAR and 2016 WAR, and did the same for 2016 to 2017. This gave me two year-to-year improvements for each player, and I threw both values onto the scatter plot below, with Blackmon highlighted in purple.

Players generally don’t see improvements like this in back-to-back seasons; Blackmon is about as far to the top-right as you can get in this plot. Of course, value can come from many different places, and a player might make large defensive improvements one year and large offensive improvements the next. While Blackmon did see some improvement in his defensive metrics this season, the bulk of his improvements have come while batting. To get the following plot, I followed the same method as above, this time for wRC+.

Again, we see Blackmon floating towards the top right. Baseball is a game of adjustments, and if a batter enjoys a period of success, pitchers will generally approach him differently to gain an advantage. This is why players generally go through cycles, following the push and pull of the game. The past few years, Blackmon seems to be part of a small group of players who have been immune to this tug-of-war effect. He has stayed one step ahead of the pitchers, not only maintaining his gains but improving upon them as time goes on.

How has he found these improvements? Between 2015 and 2016, his walk rate and strikeout rate remained fairly constant, so he must have been getting much better results on balls in play. Sure enough, his batting average increased by 37 points and his ISO increased by 65 points, giving him 49 extra points of wOBA overall. At the time, Jeff Sullivan looked under the hood and found that Blackmon’s GB% was trending downward, and he had been attacking the low strike more so than ever before. Presumably, he realized that his swing path was conducive to driving low pitches into the air, and that balls in the air are more valuable, so he made the adjustment and enjoyed a power spike.

That all makes sense, but it begs the question: how did he improve even more in 2017? If he doubled down on the fly-ball revolution, he risked becoming Ryan Schimpf or Trevor Story.

Much to my surprise, the opposite happened – his GB% actually returned back to his career average. He increased his rate of ground balls, but he still managed raised his ISO by another 42 points. Before you cry BABIP or Coors Field, I’ll briefly note that in both years, his wOBA and xwOBA increased by approximately the same amount, so something real is going on here. In this case, I think he was finding more success on batted balls based on the pitches he didn’t put in play. Stay with me here.

In 2017, Blackmon’s strikeout rate rose by about 2.5%. This is what people in the industry call “not good,” but hold on, his walk rate also rose by…about 2.5%. This isn’t a player who suddenly developed a swing-and-miss problem to sell out for power, this is a player who is intentionally going deeper into counts. When a batter is more selective about the pitches he goes after, he is putting fewer balls in play in early counts, which leads to an increase in both walks and strikeouts simultaneously.

Let’s look at it a different way: Z-swing% measures the percentage of pitches inside the zone that a player swings at, and O-swing% measures the percentage of pitches outside the zone that a player swings at. Generally speaking, you want to swing at strikes and take balls, so you want your Z-swing% to be higher than your O-swing%; the larger the difference, the better your plate discipline.

In 2016, the difference between Blackmon’s Z-swing% and O-swing% ranked in the 9th percentile – he’s always been a bit of a free-swinging leadoff hitter. But in 2017, that difference increased by 4.7%, pushing him into the 26th percentile. While he’s still more aggressive than average, he has become decidedly less so, being more selective about the pitches he attacks and remaining comfortable in deep counts. By swinging at the right pitches, he’s able to avoid the easy outs that result from poor contact on pitches outside of the zone.

We have every reason to believe that Charlie Blackmon just had a career year, and he will never sniff an MVP race again in his career. But then again, we had every reason to believe the same thing last year. When it comes to Charlie, I have some advice: if you expect him to do something, he’s probably getting ready to do the exact opposite. It’s about time we stop trying to figure him out.

“All happy families are alike; each unhappy family is unhappy in its own way.”  — L. Tolstoy

You can say something similar about baseball games. All boring games are alike; but exciting games are interesting in their own ways. Every boring game has one team building up a big early lead, which is never threatened. But there are many ways to have an exciting game: the pitcher’s duel, the slugfest, the late-inning comeback, extra innings, all in various combinations. And in between them are the bulk of games that are simply ordinary.

All of which makes ranking exciting games a tricky process, at least compared ranking to how boring they are. How does one compare Game 7 of the 1991 WS (1-0 in 10 innings) to Game 4 of the 1993 WS (15-14 in 9 innings) on the same scale? They’re great in different ways.

Back in 2005 I created a system to do just that, a rating system based simply on the runs scored in line score. I may have been the Christopher Columbus of that new world. And ranking the games allows you to rate post-season series-es.

The line-score system did work in the sense that it could tell the difference between a great game and a good one, and between a good one and an ordinary one. But while the line score gives you the basic outline of the game, it was blind to the details of what happens DURING each inning. Zero runs scored in the top of the 1st rates exactly the same; whether there were three pop-ups, or if three singles were followed by a triple play.

Eventually I realized that Baseball-Reference.com (ALL HAIL BBREF) has the play-by-play data for all playoff games, which includes a probability of victory after each play (anything that changes the outs, baserunners or score). Plotted, you can easily see if a game was good; It looks like and earthquake. If it was bad, it looks like the EKG of a corpse. Using those probabilities, we can create a much more accurate game rating. I fiddled with many rating schemes over the last 10 years before settling on one that seems both conceptually simple and that yields reasonable results.

Of course, by then I had been beaten to the basic concept by Dave Studeman (WPA) and Shane Tourtellotte (WPS). Twelve years is too long for laurels resting.

WPA = Sum(change in probability between plays)

Modified WPS = Sum(change in probability between plays) + top three plays + Final play

What I have developed is similar to their work, but I think it has some small advantages. Generally, my ratings will be quite close to Shane’s (R-squared > 99.5%). He correctly realized that simply summing the probabilities doesn’t quite work, which is why he modified it. An example…

There are seven post-season games with a WPA of exactly 4.52. Among them are:

1995 NLCS Game 2

Reds beat the Braves 6-2 in ten innings.

95 Plays, 13 plays changed the odds by at least 10%

top Play a Mark Portugal bases-loaded wild pitch +18%

70 plays with the odds in the 30% to 70% range

compared to

1960 WS Game 7

Pirates 10 Yankees 9 in nine innings

77 plays, 15 plays changed the odds by at least 10%

Of those 4 changed the odds by at least 20%

Of those 3 changed the odds by at least 30%

Of those 1 changed the odds by more than 50%

25 plays with the odds in the 30% to 70% range

There is simply no way those games are equal. The 1960 game has five different plays better than any play in the 1995 game. The 1995 game makes up the ground by (1) having 18 more plays (2) having fewer plays where nothing happened because the game was usually within one run.

1960 is still better because a +40% play isn’t twice as exciting as two +20% plays. Bill Mazeroski’s game-ending homer rates as +37%. Bobby Richardson’s game-starting line-out rates at +2%. Making a walk-off homer the equal of about 3 ½ innings with zero hits. NOPE. WRONG.

Shane accounted for this with his modified method. By counting the top three plays twice and Mazeroski’s walk-off homer three times, the ratings are now

1960: 6.49

1995: 5.19

And science prevails.

Of course, there is nothing magical about TOP THREE plays or LAST play. You could try using the top five plays and last five plays (believe me, I did).  But I do think that using Top-3 + Last can sometimes lead you astray. I will now present exhibits A and B to demonstrate where it can swing and miss.

Exhibit A: 1988 WS Game 1

Exhibit B: 1985 NLCS Game 6

I expect you to know them. The two biggest home runs in terms of changing the odds in post-season history courtesy of Mr. Clark and Mr. Gibson.

1985: WPA 4.48 in 83 plays and 9 innings

1988: WPA 3.94 in 82 plays and 9 innings

The 1985 game had more action with the same number of plays, which you can easily see in the line scores

StL          0              0              1              0              0              0              3              0              3              (7)

LA           1              1              0              0              2              0              0              1              0              (5)

Compared to

Oak        0              4              0              0              0              0              0              0              0              (4)

LA           2              0              0              0              0              1              0              0              2              (5)

The ‘85 game has a game tie in the 7^th, broken tie in the 8^th and lead change in the 9^th

The ‘88 game has a lead change in the 2^nd and a lead change in the 9^th

Modified WPS says

1985: 4.48 + 1.34 + 0.01 = 5.83 (Tied for 94^th best game)

1988: 3.94 + 1.43 + 0.87 = 6.28 (Tied for 58^th best game)

I don’t think you can argue that the 1988 game is much better than the 1985 game; I don’t think it’s a better game at all. And it’s the last-play bonus that is to blame. Had the 1985 game been played in St. Louis then Clark’s homer would have been a walk-off and the game would have rated 6.56, well ahead of the 1988 game.

If you think about it, a last-play bonus is biased towards games won by the home team. If the home team loses, the last play will rarely amount to anything.

Only 23 times has it been at least 20%. When the home team wins, it is at least 20% 122 times.

Only 11 times has it been at least 30%. When the home team wins, it is at least 20% 96 times.

I also know this because I tried last play, last five plays, and last ten plays in trying to construct a rating system. I also tried top five plays, top ten plays, all plays over 10%, WPA – .03 per play (yielding the bizarre result of games with negative excitement).

Eventually I tried a simple power transformation on EVERY play. First, I tried summing the squares of the probabilities changes, like any good statistician would.

When I did that, the 1985 game Rated 10^th and the 1988 game rated 5^th. Which is the wrong order, and both games are just rated too high. Then I tried other powers…the Goldilocks approach, looking for the one that was just right.

Power             Rank               Rank

2.0          1985       10^th         1988       5^th best game

1.9          1985       12^th         1988       8^th Best game

1.8          1985       15^th         1988       20^th Best game

1.7          1985       23^rd        1988       25^th Best game

1.6          1985       32^nd        1988       36^th Best game

1.5          1985       38^th        1988       51^st Best game

1.4          1985       53^rd        1988       76^th Best game

1.3          1985       61^st         1988       104^th Best game

1.2          1985       79^th        1988       133^rd Best game

1.1          1985       100^th      1988       158^th Best game

1.0          1985       116^th      1988      185^th Best game

Everything above 1.7 was eliminated since it rated 1988 better than 1985

Here’s some shorthand I’m going to use:

Game 6 of the 1985 NLCS: STL 7, LA 5 in 9 innings — WPA 4.48 (9-4-2-1)

Game 1 of the 1988 WS: LA 5, SF 4 in 9 innings — WPA 3.98 (5-2-2-1)

The 1985 game had 9 plays rated>= 0.1, 4 plays rated>=0.2, 2 plays rated>=0.3 and 1 play rated >=0.5

The 1988 game had 5 plays rated>= 0.1, 2 plays rated>=0.2, 2 plays rated>=0.3 and 1 play rated >=0.5

For a sense of scale, the average game is WPA 2.67 (4.89-0.88-0.33-0.03)

(You can check the examples listed below on BBRef to get more detail on each game)

Checking 1.7, both exhibits rated higher than

Game 2 of the 2017 WS: HOU 7, LA 6 in 11 innings — WPA 5.30 (10-5-3-0)

Game 1 of the 2015 WS: KC 5, NYM 4 in 14 innings — WPA 6.36 (16-3-1-0)

1.7 weights the big plays too much

Checking 1.6, both test games rated higher than

Game 6 of the 1986 WS: NYM 6, BOS 5 in 10 innings — WPA 5.14 (16-3-3-0)

Game 6 of the 1986 NLCS: NYM 7, HOU 6 in 16 innings — WPA 5.80 (11-3-2-0)

1.6 weights the big plays too much

Checking 1.5,

the 1985 game rated higher than

Game 6 of the 1986 WS: NYM 6, BOS 5 in 10 innings — WPA 5.14 (16-3-3-0)

The 1988 game rated higher than

Game 4 of the 2001 WS: NYY 4, ARI 3 in 10 innings — WPA 4.58 (10-3-2-0)

1.5 weights the big plays too much, but it’s getting hard to find clear mistakes

Checking 1.4,

the 1985 game rated higher than

Game 3 of the 1976 NLCS: CIN 7, PHI 6 in 9 innings — WPA 4.72 (14-3-2-0)

Lead changes in the 7^th, 8^th and 9^th innings.

The 1988 game rated higher than

Game 4 of the 1986 ALCS: CAL 4, BOS 3 in 11 innings — WPA 4.64 (7-4-2-0)

1.4 weights the big plays too much, but I’m now splitting hairs

Checking 1.3, I like this one. Let me check 1.2

Checking 1.2,

the 1985 game rated lower than

Game 2 of the 1996 ALDS: NYY 5, TEX 4 in 12 innings — WPA 5.02 (8-2-0-0)

Game 2 of the 1990 WS: CIN 5, OAK 4 in 10 innings — WPA 4.50 (10-2-0-0)

1.2 weights the big plays too little. Famous games are losing to games without any highlights.

So, I think 1.3 is the sweet spot.

My rating score is = Sum((change in probability between plays)^1.3) *2

The *2 at the end is purely cosmetic. It allows the very best game to score close to ten.

With base WPA, Gibson’s homer (.87) is worth about 25x a normal play (.035). With WPS it’s worth bout 75x a normal play. Raising all the plays to the 1.3 power means that Gibson’s homer is now worth about 65x a typical play.

With base WPA, Clark’s homer (.74) is worth about 21x a normal play (.035). With WPS it’s worth bout 42x a normal play. Raising all the plays to the 1.3 power means that Clark’s homer is now worth about 53x a typical play.

With a little algebra,

WPA:  Gibson = 1.18 * Clark

WPS: Gibson = 1.76 * Clark

Power 1.3: Gibson = 1.23 * Clark

A nice property of the transformation is that when the change in odds doubles, the play is worth ~ two and half times a much (2.46x)

EXCITEMENT IS NOT LINEAR

A 10% play is now worth 2.46 times as much as 5% play

A 20% play is now worth 2.46 times as much as 10% play

A 50% play is now worth 2.46 times as much as a 25% play

The system has a single parameter applied to ALL plays, so a game isn’t screwed if it has four great plays or the best play comes in the 8^th inning. Ranking games this way, here are the five games better than, and worse than, my two test cases.

I hope you’ll look at these and see that while they have different shapes, they all contain a similar ‘volume’ of excitement.

Another way to evaluate the method is to look at games with the same WPA. Going back to where I began in this article, here are the seven games with a base WPA of 4.52 (No promises that BBRef has not revised the scores since I captured the data…). They are each tied for the 108^th highest WPA. But after using the 1.3 power factoring, you get this:

1960 gets the love it deserves, moving up 56 spots to the 52^nd best game. That despite of having the fewest plays in the 30%-70% victory range. Games with more plays do worse since that means they have smaller impact plays on average. Think of the Top 5 plays as the highlight reel for the game. 1995 NLCS Game 2 has no play >0.2 and therefore drops 31 spots in the rankings.

Adjusted WPS? Weighted WPS? Power WPS? I really do need to give it a proper name.

A Final example, from among the greatest Playoff games ever.

2000 NLDS G3: Mets 3, Giants 2 in 13 innings — ModWPS Rank = 11, PowerWPS Rank = 22

1986 ALCS G5: Red Sox 7, Angels 6 in 11 innings — ModWPS Rank = 22, PowerWPS Rank = 12

1980 NLCS G5: Phillies 8, Astros 7 in 10 innings — ModWPS Rank = 25, PowerWPS Rank = 14

The 2000 game had the higher WPS, partly because it had more plays. ModWPS likes it more due to the additional action and walk-off homer, which the better top-three plays in 80/86 could not overcome.

year        WPS      Plays      Last Play    Top-3     ModWPS

2000       6.34        109         0.42          0.98                 7.74

1986       5.86       97           0.05             1.42                 7.33

1980       6.06        93           0.04            1.11                 7.21

So why do I think 1986/1980 are better?

Because, the deeper you go beyond the top three, the better the other two are revealed to be.

2000                                       1986                                       1980

1.28                                        1.94                                        1.61                        Sum of Top-5 Plays

42-31-25-16-14                  73-35-34-32-20                  40-38-35-26-24     Top-5 Plays

1.88                                        2.77                                        2.43                        Sum of Top-10 Plays

16-3-2-0                               14-5-4-1                               17-6-3-0               10%-20%-30%-50% plays

Or simply check the line scores.

2000

0 0 0 2 0 0 0 0 0 0 0 0 0 (2) Giants

0 0 0 0 0 1 0 1 0 0 0 0 1 (3) Mets

1986

0 2 0 0 0 0 0 0 4 0 1         (7) Red Sox

0 0 1 0 0 2 2 0 1 0 0         (6) Angels

1980

0 2 0 0 0 0 0 5 0 1             (8) Phillies

1 0 0 0 0 1 3 2 0 0             (7) Astros

The 2000 game IS a fabulous game. But the 1986 and 1980 games are more epic, with all the late-inning heroics. The 2000 game has exactly the required three big plays and the walk-off. It checks all the boxes.

I do kinda feel bad writing this. It sounds like I’m just picking on modified WPS here. LOOK AT WHAT ELSE IT GOT WRONG…

But as I said before, Power WPS is barely better. And to show that it’s better at all, I need to show those rare cases where it makes a better call. And it was an excellent benchmark, comparing differences between it and my sixty-eleven schemes helped me identify the flaws in sixty-ten of them.

Of course, even this is not the perfect system. Any play-by-play method will still fail to capture the in-play action. A bases-empty foul pop-out rates exactly the same as a bases-empty thrown-out-at-home-trying-to-stretch-a-triple. But it is the best we can do for now.

Whereas I used to guess my line score method captured maybe 70% of the excitement of a game, PBP ratings must be capturing upwards of 90%. Which means greater confidence in game rankings and playoff series ratings.

Anyway, if anyone has any thoughts, feedback, or questions I’d love to hear them. If no one can shoot the idea full of holes, or even one hole; then comes ranking and lists of games and series.

There is a segment of the population of the United States which meets the following criteria:  between the age of 18-21, devout FanGraphs reader, and was mesmerized by the movie “Moneyball.”  I have read the book and watched the movie a number of times, as well as dedicating time to understanding the guiding principles in the book and how they relate to professional baseball.  The relationship between on-base percentage and scoring runs in Major League Baseball is well established, but has anyone ever taken the time to examine the relationship at the collegiate level?

Collegiate baseball is volatile — roster makeups change dramatically each year, no player is around more than five years, not to mention there are hundreds of teams competing against one another. In terms of groundbreaking sabermetric principles, this study is not intended to turn over any new stones, but rather present information which may have been overlooked up to this point, which is the relationship between on-base percentage and runs scored in collegiate baseball.

To conduct this study, I compiled a list of Southeastern Conference team statistics from the 2014-2017 seasons (Runs Scored, On Base Percentage, Runs Against, and Opponents’ On Base Percentage).  I then performed linear regression on the distribution by implementing a line of best fit.  Some teams’ seasons were excluded due to inability to access that season’s data, and I felt like removing the 2014 Auburn season on the grounds that it was an outlier affecting the output (235 runs, 0.360 OBP).  Below is the resulting math:  the R², and the resulting predictive equation:

pic.twitter.com/4Fib3YNqhj

— T Hooper (@thooperfangraph) December 8, 2017

Runs Scored = ( 3,537. x OBP ) – 933.6791

R² = 0.722849

I am by no means a seasoned statistician, but in my interpretation of the R² value, the relationship between Runs Scored and OBP in this is moderately strong, with a team’s OBP accounting for roughly 72.3% of the variation in Runs Scored in a season.  Simply, OBP is statistically significant in determining the offensive potency of a team.

At the professional level, the R² is found to be around 0.90.  The competitive edge the Oakland A’s used in “Moneyball” was using this correlation to purchase the services of “undervalued” players.  But what about in college?  Colleges certainly cannot purchase their players, but the above information can be useful to college programs.

For example, the average Runs Scored per season of the sample I used was roughly 347.8.  If an SEC team wanted to set the goal of being “above average” offensively, they would be able to determine, roughly, what their target OBP should be by using the resulting predictive equation from the Linear Fit:

pic.twitter.com/icI3tbcX78

— T Hooper (@thooperfangraph) December 8, 2017

Does this mean if an SEC program produces an OBP of .362 they would score 348 runs precisely? Obviously not. Could they end up scoring exactly 348 runs? Yes, but variation exists, and statistics is the study of variation.  Here are a few seasons in which teams posted an OBP at or around 0.362, and the resulting run totals:

pic.twitter.com/XaR2Ed1s6P

— T Hooper (@thooperfangraph) December 8, 2017

The average of those six seasons’ run totals was 347.5, which is pretty darn close to 348, and even closer to the average of 347.8 runs derived from the sample.

Another use for this information is lineup construction and tactical strategy in-game.  The people in charge of baseball programs do not need instruction on how to construct their roster and manage their team, but who would disagree with a strategy of maximizing your team’s ability to get on base?

The purpose of this study was to examine the relationship between On Base Percentage and Runs Scored in college baseball, and how the relationship compares to its professional counterpart.  To conclude, the relationship between OBP and runs exists at the collegiate level, and carries considerable weight and value if teams are willing to get creative in utilizing its ability.

Disclaimer: I am a beginner-level statistician, and if you have any suggestions or critiques of this article, please feel free to share them with me.

Theodore Hooper is a Student Assistant, Player Video/Scouting, for the University of Tennessee baseball program.  He can be reached at thooper3@vols.utk.edu or on LinkedIn at https://www.linkedin.com/in/theodore-hooper/

Because I believed Jeff Sullivan that there was a 2% chance Ohtani would sign Friday, I wrote this article, that now reads a bit weird, but I’m not going to change it because I have to get back to the job that I do for money instead of for fun. Any complaints about said weirdness should be addressed to Jeff Sullivan.

The phrase “sure thing” is thrown around a lot in sports to describe things that are not actually sure things. Atlanta was going to win the Super Bowl. Up 25 points, it was a sure thing until it wasn’t. Shohei Ohtani is not a sure thing to be a superstar in MLB, or even an All-Star. He’s not even a sure thing to be an average big-league player. History is littered with players that were star players one day and also-rans the next. But what Ohtani is a sure thing to do is pay off his price tag. Barring a tragic accident or something else outside of the realm of baseball, the team that signs Ohtani will surely earn more than $20.5 million extra during the 2018 baseball season that they would not have earned without him.

To understand why this is a certainty, you can look at Stephen Strasburg in 2010. The Nationals were the worst team in baseball in 2009, finishing with 59 wins for the second consecutive season. They were not much better in 2010, adding 10 more wins to that total, but still finishing 5^th in the NL East. Their overall attendance for 2010 was bad, which is to be expected. 1,828,066 were said to have paid money to see the Nationals play that year, or about 22,500 per game. That was only about 10,000 higher, on the season, than they managed in 2009. Strasburg’s first game was electric (I was there). It was the kind of atmosphere that made no sense at all for a team that was mired in long-term terribleness. The game, played on a Tuesday, sold out with attendance only rivaled that season by opening day, up to that point.

But that only tells a small part of the story. The two Tuesday games the Nationals had played at home prior to this game averaged about 16,000 fans. It’s pretty safe to say that this game, against the Pirates, would have been in a similar range without Strasburg. But the Strasburg effect was much wider reaching. First, once it became known that Strasburg was pitching, the Nationals created a ticket package to sell many of the unsold tickets. The package included the Strasburg game, plus three additional games. For those that came to the stadium without a ticket, their choices were between that and the suddenly busy scalpers.

The weekend before Strasburg’s debut, the Nationals drew almost 91,000 fans against the Reds. That was about 6,000 more than they drew during their previous weekend series against the Orioles. But the Orioles are not really a fair comparison because they are Beltway rivals of the Nationals. If you go back the weekend series before that, when the Nationals were playing the Marlins, just over 63,000 came to the ballpark. Now, some of this might have been random or based on other factors, but fans were anticipating Strasburg’s debut and buying tickets in anticipation of it — fans including the humble author of this post. Some even speculated that the Nationals intentionally misled fans in order to juice the gate.

Strasburg’s second start was in Cleveland, who also seemed to have benefited from a large Strasburg-related spike in attendance for that game. His third game was a Friday and was sold out. Again, even weekend games prior to Strasburg were sparsely attended. The Saturday game after his start was nearly sold out, likely due to fans incorrectly believing it was going to be the day Strasburg pitched. His 3^rd home game was the first that did not sell out. But there is more to the story (again, I was there). This game was played during the week, during the day, and it was literally at or above 100 degrees that day. There were still almost 32,000 fans there. After pitching on the road, Strasburg again sold out the stadium for his 6^th start and had a large, but not sold-out game for his 7^th. His next home start, another Tuesday game, again sold out. I’ve probably gone on long enough, if not too long. If you didn’t know how much Strasburg boosted attendance, you do now.

And here is the question: As much hype as there was in D.C. during the summer of 2010, do we really think that Ohtani’s hype won’t meet or perhaps even far exceed it? I certainly do. Additionally, the team that signs Ohtani will have a much easier time plotting and planning exactly how they can wring the most dollars possible out of their fans. In 2010, the Nationals created a multigame package on the fly, but now teams are exploring more and more ways of earning extra dollars through dynamic pricing. If a team like Seattle were to get Ohtani and announces that he’ll be pitching the second game of the season, the added revenue they would be able to earn would be astounding. Last season, the Mariners’ attendance dropped from 44,856 on opening day to 18,527 the next day. With Ohtani, it’s safe to say they’d sell out their second game.

If you’ve stayed with me this long, you might be wondering “yeah, I knew all of this already, so what?” And yes, most FanGraphs readers probably already believed that Ohtani is going to really juice attendance wherever he ends up. But then, why did three teams not even bother to try to acquire him? There simply is no justification. If you are the Marlins, you can do both things. You can say you are cutting payroll by $50 million, but at the same time if Ohtani for some reason picks the Marlins (he wouldn’t), you find the $20 million to pay him. You will get it back and more. Take out a loan. Heck, take out a payday loan with onerous and unfair terms and you will still end up ahead. It simply makes no sense. He is, from a financial standpoint, literally a sure thing.

Earlier this season I set out to build a tool similar in nature to my dSCORE tool, except this one was meant to identify swing-change hitters. Along the course of its construction and early-alpha testing, it morphed into something different, and maybe something more useful. What I ended up with was a tool called cHit (“change Hit”, named for swing changers but really I was just too lazy to bother coming up with a more apt acronym for what the tool actually does). cHit, in its current beta form, aims to identify hitters that tend to profile for “impact production” — simply defined as hit balls hard, and hit them in the air. Other research has identified those as ideal for XBH, so I really didn’t need to reinvent the wheel. Although I’d really like to pull in Statcast data offerings in a more refined form of this tool, simple batted ball data offered here on FanGraphs does the trick nicely.

The inner workings of this tool takes six different data points (BB%, GB%, FB%, Hard%, Soft%, Spd), compares each individual player’s stat against a league midpoint for that stat, then buffs it using a multiplier that serves to normalize each stat based on its importance to ISO. I chose ISO as it’s a pretty clean catch-all for power output.

Now here’s the trick of this tool: it’s not going to identify “good” hitters from “bad” hitters. Quality sticks like Jean Segura, Dee Gordon, Cesar Hernandez, and others show up at the bottom of the results because their game doesn’t base itself on the long ball. They do just fine for themselves hitting softer liners or ground balls and using their legs for production. Frankly, chances are if a player at the bottom of the list has a high Speed component, they’ve got a decent chance of success despite a low cHit. Nuance needs to be accounted for by the user.

Here’s how I use it to identify swing-changers (and/or regression candidates): I pulled in data for previous years, back to 2014. I compared 2017 data to 2016 data (I’ll add in comparisons for previous years in later iterations) and simply checked to see who were cHit risers or fallers. The results were telling — players we have on record as swing changers show up with significant positive gains, and players that endured some significant regression fell.

There’s an unintended, possible third use for this tool: identifying injured hitters. Gregory Polanco, Freddie Freeman, and Matt Holliday all suffered/played through injury this year, and they all fell precipitously in the rankings. I’ll need a larger sample size to see whether injuries and a fall in cHit are related or if that’s just noise.

Data!

Okay, so here’s the breakdown. I pulled all 2017 hitters with 400 at-bats or more so I could capture some significant hitters that didn’t have qualifying numbers of ABs due to injury. Ball-bludgeon extraordinaire Joey Gallo is a pretty solid name to have heading up this list, as he’s pretty much the human definition of what this tool is trying to identify. JD Martinez, Aaron Judge, Cody Bellinger, Miguel Sano, Trevor Story, and Justin Turner all in the top 10 is pretty much all the proof-of-concept I needed.

Interesting notes:

Brandon Belt at 12 — Someone needs to tell the Giants to trade him to literally any other team, stat.

Giancarlo Stanton at 46 — Surprisingly, the MVP fell off from his stats in 2016. His grounders and soft contact rose by 3 or more percentage points, and shaved off the equivalent from hard and fly balls. His output was fueled by adding almost 200 ABs to his season — he could actually get better if he can stay healthy and add those hard flies back in!

Francisco Lindor at 58 — The interesting part of this is even though Lindor is still a decent way down the list, he actually was the biggest gainer from last season to this, adding 9.52 points to his cHit. We knew he was gunning for flies from the outset of the season, and it looks like his mission was accomplished.

Mike Moustakas at 87 — Frankly, being bookended by Jose Ramirez and Andrew Benintendi should, in a vacuum, should be great company. But this is a prime example of how cHit requires users to not take the numbers at face value. Ramirez and Benintendi aren’t slug-first hitters like Moose. They’ve got significantly better Speed scores, plus aren’t as prone to soft contact. I’d be very wary of Moose regressing, as he seems to rely on sneaking some less-than ideal homers over fences. If he goes to San Francisco I could see his value crater (see Belt, Brandon).

Eric Hosmer at 206 — Nope, negative, pass, I’m trying to sign quality hitters here <— Suggested responses for GMs when approached this offseason by Scott Boras on behalf of Hosmer.

Final Notes:

•  Batted-ball distribution data is noticeably absent. In one of my iterations I added in those stats, and found that they actually regressed the accuracy of the formula. It doesn’t matter where you hit the ball, as long as you hit it hard.
• Medium% and LD% are noisy stats. They also regressed the formula.
• I may look to replace BB% in future iterations. For now though, it does a decent job of capturing plate discipline and selectivity.
• K% doesn’t seem to have much of an impact on cHit (see Gallo, Joey).
• R-squared numbers over the last four years of data hold pretty steady between .65 and .75, which is really encouraging. Also, the bigger the pool of data per year (number of batters analyzed), the higher R-squared goes; which is ultimately the most encouraging result of this whole endeavor.

Input is greatly appreciated! I’m not a mathematician in any stretch of the imagination, so if there’s a better way of going about this I’d love to hear it. I’ll do a writeup about my swing-change findings at a later date.

We saw an unprecedented jump in home runs in the last few years. What made it so strange was that most of it happened after the 2015 All-Star break. There is an increased awareness of launch angle and bat path, and 2015 was the first year there was a public in-game feedback, but still you would expect such an adjustment to take longer, especially since in-season swing changes are really hard to do — maybe with a whole offseason to work on it, it might have been slightly more believable.

There have been multi-factor explanations like a great rookie class of power hitters in the second half of 2015, changed approach, and other stuff like a slightly smaller zone, but really you would not expect such a multi-factor cause to happen that quickly and distinctly between two season halves. That made most sabermetric writers, including most of the FanGraphs staff, believe in a single-factor cause, most likely the ball.

There is some evidence for a changed ball, and there is also anecdotal evidence of minor-league players called up claiming the MLB ball flies farther. However, MLB so far has rejected that, and supported that with the credible name of professor Alan Nathan, albeit without really publishing the data, which further increased the suspicion.

We also did see an increase in launch angle: In 2015 in the first half, the LA of the league was 9.6, and in the second half it was 10.3, which further slightly increased in the first half of 2016 (10.4) and 2017 (10.8). The biggest jump, however, occurred between the season halves of 2015. So were the players really able to increase their LA with a single focus cue without really having much time to work on swing mechanics by just aiming higher after getting the first-half feedback? Those are the most talented athletes in the world, but still that sounds incredible.

But of course just increased elevation doesn’t explain the surge. The number of balls hit between 20 and 35 degrees (usual HR range) increased from roughly 8200 in the first half of 2015 to roughly 8600 in the first half of 2016, but the number of HRs increased from 2521 to 3082. Since less than half of the FBs between 20 and 35 go out of the park (I don’t have the exact number but I estimate 30% from the numbers I have), the 600 more batted balls in that range don’t explain 500 more HRs. That means, apart from more FBs, those also got out more, and the league saw a jump in HR/FB rate (9.5% in 2014 and 12.8 in 2016).

To research that, I looked into some Statcast stats. All stats here are just first halves of the respective seasons, because the first half of 2015 was the last “normal” HR half. Also I want to lessen weather effects.

This table shows that balls between 20 and 35 degrees do indeed fly farther and also go faster off the bat.
Average distance (20-35 LA)

2015 326 89.9
2016 331 91.6
2017 332 91.3

So does this jump in HR/FB prove a juiced ball? Not necessarily. To explain this, we have to get into swing mechanics. The attack angle is the vector of the bat’s sweetspot just before contact. Generally you can hit higher LAs (launch angles) by just hitting the bottom of the ball, but while some backspin is good, too much of it will slow down the ball. Generally the more LA and attack angle match, the higher the exit velo. That means players that try to swing up more might shift their highest velos to higher LAs. So while players couldn’t really change their swings that fast, just the intent of higher LA might have unconsciously caused a higher attack angle and thus more “flush hit” fly balls.

Evidence for the ball not being a factor is that average league EV is actually down a tiny bit. However, if the attack-angle theory is true, you would also expect that the EV of balls between 0 and 10 degrees would lower a little bit, and that hasn’t really happened.

Avg EV EV (0-10 LA)
2015 87.1 93.3
2016 87.8 93.3
2017 86.9 93.1

Another theory came from Tom Tango. He assumed that harder swinging and increased attack angles lead to higher peak EVs but also more weak mis-hits.

We do indeed see a big increase of balls hit above 105 MPH, but on the other side (and there have to be weaker hits to explain that overall EV is not up) there is an effect of more weak-hit balls in 2017, but not so in 2016.

EV >85 Balls 105
2015 96.2 19210 2960
2016 96.9 19075 3917
2017 96.7 20436 3635

To see if there is an aerodynamic effect — one theory of the juiced ball is reduced air drag due to lower seams — I looked at the average distance of balls hit at 20-25 degree LA in different velocity buckets.

EV Range 95-100 100-105 105-110
2015 366 391 415
2016 362 387 408
2017 363 391 411

You can’t really see an effect here. Balls hit at the same EV (which is measured right after exit so that air drag hasn’t done its work yet) don’t fly farther in 2016 or 2017 than they did in the first half of 2015. That means there likely isn’t really an effect of aerodynamics, at least not a big one.

So the reason for increased HRs seems to be mostly that fly balls fly faster and farther for whatever reason. We don’t see an across-the-board increase of EV, however, but simple explanations like a shift of max EVs to other launch angles don’t seem to really work either, as LAs from 0-10 (and also lower than minus 5 for that matter) haven’t really changed in their EV.

It remains mysterious what did actually happen. We do know LAs have increased some, but that doesn’t explain the whole story. But I couldn’t find real evidence for a changed ball in Statcast either. Could a super fast on-the-fly adjustment of the league between season halves based on the Statcast date really be the driving factor here?

Intellectually I really want to believe the juiced-ball theory, as it is the most elegant explanation for such a quick turnaround, but maybe it isn’t that easy.

Yasiel Puig had an impressive rebound season in 2017. He responded to disappointing, injury-marred seasons in 2015-16 with a solid 2.9 WAR this year. Puig greatly improved his plate discipline, increasing his selectivity and his contact rates en route to an 11.2 BB% and 17.5 K%. He has been known for his free-swinging ways since entering the league, but he may have changed that reputation this past season. Puig was not the reckless hitter he had been in the past. However, he may have decided to channel that recklessness to the base paths.

Puig is a good athlete, but has never been much of a base-stealer. In his first two years, he converted a poor 22/37 of his steal attempts, and mostly quit trying to steal in 2015-16. Despite the failures of his base-stealing, he had actually been a slight positive on the bases in his career, accumulating 0.5 runs above average in 2013-16, per FanGraphs’ base-running metric. Puig reverted to his aggressive base-stealing in 2017, and his 15 stolen bases indicated success with the approach. His 71.4% conversion rate was not exceptional, but not horrible. But his base-running had no semblance of success.

Puig was the sixth-worst player on the bases in 2017, accumulating -7.6 runs. He was surrounded by names like Albert Pujols, Miguel Cabrera, and Edwin Encarnacion. Not exactly names you want to be grouped with when talking about base-running.

FanGraphs’ base-running metric encompasses three things: wSB, wGDP, and UBR. wSB measures the run value a player produced based off attempting steals. Puig produced a mediocre mark of 0.1, which lines up with his stolen-base numbers. wGDP measures the ability of a player to avoid double plays. Puig ranked 13th-worst in 2017 with -2.4 runs produced, but wGDP is more related to avoiding ground balls with men on base and beating out throws to first. UBR (Ultimate Base Running), measures the value of a player with respect to non-stealing base-running, like taking an extra base. Puig produced -5.3 runs per UBR, sixth-worst in the league.

Let’s focus on that UBR. Providing context, that figure is a whole lot worse than sixth-lowest in the league. Puig had a speed score of 4.4 in 2017, placing him 80th in the league, among players with at least 400 PA. The five players directly ahead of Puig in UBR had an average speed score of 2.3. That would rank 185th. Considering speed, Puig was likely the worst base-runner in the league. He did things like this, which you probably remember from the World Series:

Puig was probably the worst base-runner in 2017. But how bad was he on a historical level?

Of all individual seasons (min 400 PAs) since 2002, when UBR was introduced, Puig’s UBR ranks lower than the 3rd percentile out of 3393 seasons. Of those individual seasons with a speed score within one standard deviation of Puig’s, his UBR ranks lower than the 1st percentile.

Here is a plot of every one of those seasons, with each player’s stolen-base total versus their UBR. Puig in 2017 is highlighted in yellow.

Obviously, players with higher stolen-base totals are generally faster, and thus produce more value on the bases. As with anything, though, there are outliers. Puig is definitely an outlier. Only one player with as many stolen bases has produced an UBR lower than Puig: Juan Encarnacion in 2003. Here is another chart, with speed score plotted against UBR. Puig again is in yellow.

Puig is again an extreme outlier, even historically. Considering his athleticism, Puig had one of the worst base-running seasons of the last 15 years. This does not mean a ton. Puig has not always been a terrible base-runner, and he was still a quite effective player in 2017, woes on the basepaths aside. He can easily turn it around and produce a solid base-running season with the physical gifts he has. However, in 2017, Puig’s base-running was really, really terrible.