What Makes an Effective Pitcher<\/h3>\n
A pitcher\u2019s job is to keep the opponent from scoring enough runs to defeat his team. That is reached in three main ways: keeping runs off the board, getting opponents out, and pitching with command (the process<\/strong>). However, many of the stats that gain the most attention focus on the product (win, loss, save) rather than the process. Pitchers with good win\/loss records might not have done so by being a dominant pitcher but rather by being the guy fortunate enough to be on the mound while his team out-slugged their opponent.<\/p>\n With relievers, the process statistics are even more important than the product statistics. This is because the only positive \u201cproduct\u201d statistics tend to go to the closers and set-up men. However, the negative \u201cproduct\u201d statistics can go to anyone \u2014 long relievers, middle relievers, set-up men, and closers alike. Therefore, we must, when evaluating relievers, focus on the statistics that measure how well a reliever does the three main jobs of a pitcher. We must look more intently at statistics that measure run prevention, avoiding baserunners, and pitching with command.<\/p>\n The main job of a pitcher is to prevent the other team from scoring runs. Since this section will be the longest and most detailed, we will come back to it at the end.<\/p>\n How do pitchers keep runs off the board? By getting guys out \u2014 in other words, avoiding baserunners. If a batter isn\u2019t put out, he reaches base, almost exclusively via walk or hit. The stat that measures how many baserunners a pitcher allows per inning is Walks and Hits per Inning Pitched (WHIP). This stat has been used for decades, and the first sites to use it have used three decimal digits.<\/p>\n Obviously, the lower a pitcher\u2019s WHIP, the better. The league average hovers in the 1.300s every season. A WHIP in the 1.200s will get a pitcher on an All-Star Team; a WHIP in the 1.100s, if done consistently, will get a pitcher into the Hall of Fame.<\/p>\n Records: Single season \u2014 Pedro Martinez<\/a><\/strong>, 2000 (0.737); Career (1000 IP min.) \u2014 Addie Joss<\/a><\/strong>, 0.968<\/em><\/p>\n A common criticism of WHIP is that it only measures walks and hits but not what type<\/em> of hit. However, that is not the goal of the stat. It\u2019s simply to measure how many baserunners a pitcher allows per inning. To see what type <\/em>of hit a pitcher allows, we need something else.<\/p>\n Two statistics work together to show us that. One is extra-base hit percentage (XBH%), which measures how often a pitcher gives up an extra-base hit. To calculate this stat, take the number of extra-base hits (doubles, triples, and home runs) a pitcher allows and divide it by the total batsmen faced (TBF or BF).<\/p>\n The other statistic is X\/H%, which shows the percentage of a pitcher\u2019s hits allowed that go for extra bases. To calculate this, use the same formula as XBH%, but instead of dividing extra-base hits allowed by total batsmen faced, divide them by the number of hits a pitcher allows.<\/p>\n The most effective pitchers force their opponents to hit their way on base. In other words, they pitch with control, striking out opponents more often than they walk them. (Note: Contact pitchers, while effective and skilled, rely on several factors beyond their control, so their command and control are hard to measure.<\/em>) One of the best relievers at this is Kenley Jansen<\/a><\/strong>, who has struck out 1,107 in his career while walking 226 \u2014 a strikeout-to-walk ratio of 4.90.<\/p>\n Only six relievers (minimum 600 innings pitched, at least 80% of appearances came in relief) have ratios of four or greater: Jansen, Liam Hendriks<\/a><\/strong>, Rafael Betancourt<\/a><\/strong>, Sergio Romo<\/a><\/strong>, Jonathan Papelbon<\/a><\/strong>, and Mariano Rivera<\/a><\/strong>. For reference, the league average ratio for relievers from both leagues in recent years has been around 2.50.<\/p>\n To measure command, statisticians came up with strikeouts per nine innings (K\/9) and walks per nine innings (BB\/9). This takes a pitcher\u2019s strikeouts, multiplies them by nine, and divides the answer by the number of innings he pitched. For BB\/9, it does the same, except it uses walks instead of strikeouts.<\/p>\n There\u2019s only one problem with using these stats: not all innings are the same length. Furthermore, longer innings sometimes aren\u2019t a pitcher\u2019s fault. Errors \u2014 fielding, catching, throwing, or mental \u2014 are notorious culprits for extending an inning.<\/p>\n Or consider this scenario: two pitchers strike out the side. One does so in 1-2-3 fashion, while the other strikes out three batters but allows five hits. Which pitcher was more dominant? Obviously, the first one was, but these two will have the same amount of K\/9.<\/p>\n The solution to this statistical hole comes in three statistics that are used in conjunction with each other. They are strikeout percentage (K%), walk percentage (BB%), and strikeout minus walk percentage (K-BB%). Strikeout percentage states what percentage of all batters faced by a pitcher end up striking out. It is calculated by dividing a pitcher\u2019s strikeouts by the total number of batsmen faced. Walk percentage does the same thing and is calculated the exact same way. The third stat shows the difference in percentage points between the two.<\/p>\n For relievers, using percentages instead of \u201cper nine\u201d stats to measure strikeouts and walks is important. This is because they pitch so many fewer innings than starters \u2014 both in a season and per outing. Consequently, a minor stat variation can cause a huge swing in per nine stats.<\/p>\n Now for the granddaddy of all reliever stats but also the one that is among the most flawed: the save. Saves can be incredibly deceptive. This comes, in great part, due to the qualifications to get one. It can only go to a finishing relief pitcher who enters the game with a lead that he never surrenders. He must also do one of the following: pitch at least three innings without getting the win; enter the game with the tying run either on base, at bat, or on deck; enter with a lead of three runs or fewer and pitch at least one inning. Any pitcher who enters the game in a save situation and then surrenders the lead gets a blown save.<\/p>\n Middle relievers who would never, ever get the save simply due to their roles can get a blown save. Saves can also go to pitchers whose sole accomplishment is that they didn\u2019t blow a lead \u2014 three runs in one inning \u2014 that teams hold 24 out of 25 times. (Win Probability for teams who enter the last inning with a three-run lead is 96%.) Pitchers who enter on the winning side of a blowout in the seventh inning and finish the game also get the save, regardless of the final score, so long as they don\u2019t lose the lead.<\/p>\n Take this scenario. We are in the top of the seventh inning of a tie game. Our opponents have loaded the bases. There are no outs, and the 3-4-5 hitters are due up. Adam comes in to pitch and gets us out of it, striking out the side on 10 pitches. We do not score in the bottom of the seventh. Bruce comes in to pitch the top of the eighth. He coughs up a leadoff double, a fly ball to the warning track, an RBI single, another single, an infield popup, and another fly to the track. Inning ends, we trail by one. Sam hits a grand slam in the bottom of the eighth to give us a three-run lead.<\/p>\n Charles comes in to close and walk the leadoff hitter. A wild pitch puts him on second. The next hitter crushes a ball to the track, and the runner tags on the fly out. Next hitter belts a two-run homer, cutting our lead to one. I give up a single. The next hitter rips an atom ball liner to the first baseman. Since he was holding the runner on, he was right by the bag, so he stepped on first for a lucky game-ending double play.<\/p>\n Bruce didn\u2019t pitch all that well, yet he got the win since he was lucky enough to be the last guy on the mound when Sam hit the grand slam. Charles got the save, even though we won despite<\/em> his best efforts. And Adam, the pitcher who contributed the most to our win, got nothing.<\/p>\n Nate Silver of fivethirtyeight.com saw the flaws in the save, including the scenario I just described, and created a statistic for clutch late innings. This fills in the holes left by the save. Long-time readers of my analyses are quite familiar with it \u2014 the Goose Egg.<\/p>\n Goose Eggs (GE) go to relief pitchers only, and they are awarded by inning. They may only be recorded in the seventh inning or later. To get a Goose Egg, a pitcher must meet the following criteria:<\/p>\n If a pitcher in a Goose Egg situation has an earned run charged to him, he gets a Broken Egg (BE). When allowing an inherited or unearned run to score, he gets a Meh (M). If he allows an earned run in an inning where he closes out a victory for his team, he also gets a Meh.<\/p>\n According to Silver, the most important Goose Egg-related statistic is the ratio of Goose Eggs to Broken Eggs (GE\/BE). The historical average, dating back to 1921, is 3.0 to 1. For decades, the AL and NL averages have both held true to that, as they have been close to 3.0 to 1.<\/p>\n The main job of a pitcher is to prevent the other team from scoring runs. When showing how many runs a pitcher has allowed, there are two stats: runs and earned runs. Earned runs are runs that are scored without the aid of a fielding, throwing, or catching error by the defense. In other words, they\u2019re the runs that are, for lack of a better term, a pitcher\u2019s fault.<\/p>\n Raw run totals do not tell the full story. Pitchers who pitch more often will almost always give up more cumulative runs. To counter that fact, we need a \u201crate\u201d stat for run prevention. Baseball has used Earned Run Average (ERA) for over a century to measure run prevention. Its premise is simple, measuring how many earned runs a pitcher would allow, on average, if he were to pitch a full nine-inning game.<\/p>\n However, numbers mean little without context. We need to know a \u201cgood\u201d ERA value. Over the years, it has changed. Run-scoring environments change, being affected by factors like ballpark size, elevation, number of teams in the league, and the designated hitter. Subtle factors like the baseball itself and how hot and\/or dry a summer is also come into play. To account for these, we need more.<\/p>\n Comparing a pitcher\u2019s ERA to the league average from that season gives a much better snapshot into how well he prevents runs. Taking the pitcher\u2019s home ballpark into consideration makes the snapshot even better. Baseball Reference uses ERA-plus (ERA+) to do this, while Fangraphs uses ERA-minus (ERA\u2013). ERA\u2013 is the better stat of the two. It takes a pitcher\u2019s ERA, incorporates his home ballpark\u2019s park factor, and divides it by the league average ERA. For readability, the answer is multiplied by 100.<\/p>\n ERA\u2013 is better than ERA+ for two reasons. First, the lower the number, the better \u2014 just like ERA. Secondly, ERA\u2013 compares the pitcher to the rest of the league. ERA+ compares the rest of the league to the pitcher. It might sound like it\u2019s the same thing, but it\u2019s not. 80 cents is 80% of a dollar. 80\/100 = .80 = 80%. A dollar is 125% of 80 cents. 100\/80 = 1.25 = 125%. If you ask lots of people which of these two representations makes more sense, most will say the former and not the latter.<\/p>\n These statistics are great for starting pitchers, largely because ERA was designed <\/em>for starting pitchers. That\u2019s because of how it\u2019s calculated \u2014 earned runs, multiplied by nine, divided by innings pitched. In a start that goes well, starting pitchers typically pitch between six and nine innings. Relievers, however, rarely pitch more than one inning per outing. This makes one bad inning \u2014 or, sometimes, one mistake pitch \u2014 cause long-lasting damage to a reliever\u2019s ERA.<\/p>\n Think of it this way. Say a starting pitcher pitches a scoreless first but gives up a three-run home run in the second inning. If he pitches efficiently while not giving up any more runs, chances are good that he\u2019ll last through the sixth inning. That one bad inning didn\u2019t hurt the pitcher\u2019s ERA all that much, since he had four scoreless innings to bring it back down some. Now say a reliever does the same thing. How long will it take for him to pitch enough innings to bring his ERA back down to Earth? A week? Two?<\/p>\n Take former Arizona Diamondbacks<\/a> reliever J.B. Wendelken<\/a><\/strong> for an example of how two pitches can, as The Athletic<\/em>\u2019s Zach Buchanan once put it, \u201cnuke\u201d a reliever\u2019s ERA. Wendelken joined the team August 15, 2021, pitching 18 2\/3 innings in 20 appearances, mostly in late innings. In 18 of those appearances, he gave up four earned runs over 17 innings. That comes out to a 2.11 ERA, strong by any measure. In his other two appearances, he gave up a three-run homer in Denver and a two-run homer in Seattle, a game where he still earned the save. Those two mistake pitches ran his ERA as a Diamondback up to 4.34 \u2014 deceptively high, given his performance in other games in 2021.<\/p>\n Fractional innings compound this problem. Say a reliever comes in with one out and the bases empty. Let\u2019s say he retires the first hitter, walks the second, and then gives up a two-run homer before getting the hook. Using the ERA formula \u2014 earned runs times nine divided by innings pitched \u2014 gives us 2 x 9, or 18, divided by one third. Remember fractional arithmetic \u2014 when dividing by a fraction, you invert the fraction and multiply. This means that 18 divided by one third is 18 times<\/em> 3 \u2014 54.00.<\/p>\n Since relievers typically pitch only one inning per outing, it would take at least six straight scoreless outings to pull his ERA below 3.00. This would take, at minimum, almost a week. In more likelihood, it would take closer to two.<\/p>\n So why not look, instead, at how often a reliever has a scoreless outing? We keep track of shutouts for starters, even though they don\u2019t happen very often. After all, the active leader \u2014 Clayton Kershaw<\/a><\/strong> \u2014 has 15. We know that starters can <\/em>shut an opponent out, but we don\u2019t expect them to. That is not the case with relievers. We expect<\/em> them to blank the opposition and hand the ball off to the next guy in line.<\/p>\n In short, a reliever\u2019s job is to get outs without allowing runs. There is little-to-no margin for error. Either he allowed runs or he didn\u2019t.<\/p>\n Scoreless Outing Percentage, which I personally developed in late 2021, shows how often a reliever keeps runs off the board \u2014 in other words, how often he does his job. To calculate, take the number of scoreless outings a reliever has \u2014 meaning no runs were charged to him \u2014 and divide it by his total relief appearances. Convert the decimal answer to a percent, and voil\u00e0. Be sure to have the league average for that season handy for context.<\/p>\n For reference, I also add how many appearances a reliever had where the only runs he allowed were unearned (UOtg). In addition, I include a side note with the number of scoreless outings where he allowed one or more inherited<\/em> runners to score (IOtg).<\/p>\n Example: 46 scoreless outings out of 51 total relief appearances![\"\"](\"https:\/\/sportrelay.com\/mlb\/wp-content\/uploads\/sites\/2\/2023\/04\/Reliever-Stats-Figure-A.png\")
<\/a><\/p>\nRun Prevention, Part I<\/h3>\n
WHIP: Getting Outs<\/h3>\n
<\/a><\/p>\nXBH%, X\/H%<\/h4>\n
<\/a><\/p>\nPitching with Command<\/h3>\n
Problem with K\/9 and BB\/9<\/h4>\n
The solution: K%, BB%, K\u2013BB%<\/h4>\n
<\/a><\/p>\nSaves: The Problem<\/h3>\n
Clutch Late Innings: The Goose Egg<\/h4>\n
Qualifications<\/h5>\n
<\/a><\/p>\n<\/a><\/p>\n<\/a><\/p>\nRun Prevention<\/h3>\n
ERA, ERA\u2013<\/h4>\n
Why ERA\u2013 Is Better than ERA+<\/h4>\n
<\/a> <\/a><\/p>\nProblem for Relievers with ERA and ERA\u2013<\/h4>\n
Fractional Innings<\/h5>\n
<\/a><\/p>\nReliever Solution: Scoreless Outing Percentage<\/h4>\n
\n46\/51 = .902, or 90.2%<\/p>\n