Article published in billjamesonline.com on March 25, 2019 under my name Javier Caceres

How many times have you seen a runner score but due to an error, a fielder’s choice, or some other scenario the batter failed to earn an RBI? Looking at season numbers, you probably have noticed that for all teams, every year their runs scored are greater than their RBIs. Could one of these non-RBI runs be the difference in the outcome of a game? I am sure you just intuitively answered correctly with a “yes” even if you’re unsure of the frequency with which that happens. By the end of this paper, you’ll have a criterion to determine if the methodology presented objectively defines and measures lucky runs and how those lucky runs create lucky wins.

Sabermetricians research and analyze many parts of the game and you will increase your understanding of the game if you consider two simple concepts: 1) Instead of “runs” think of “runs scored” (RS ), MLB’s official metric name and 2) as a consequence of point “1,” accept that there are two types of runs included in the “runs scored” metric: runs that are batted in (RBI) and those that are “not batted it” (non-RBI). Since non-RBI = RS – RBI, in consequence RS = RBI + non-RBI.

In Sabermetrics, there is little chance of finding a metric that records what has not happened. And it’s these non-events that often influence the final score of the game, like in the case of a metric we’ll call “runs not batted in”. These runs will be also referred to as “just lucky runs” or JLR, and if one of these runs determines the winner of the game, we’ll call that fortunate outcome “just a lucky win,” or JLW.

For the 2017 regular season, according to baseball-reference.com, 22,582 runs were scored with only 21,558 RBI, meaning that the difference of 1,024 “runs not batted in” represented 4.53 % of the scored runs. All teams benefitted from these lucky runs with as few as 22 received by the New York Mets up to 50 by the Boston Red Sox (Table 1). In the detailed analysis that follows, that we’ll show that one of these lucky runs decided 117 games.

Luck in baseball – Two respected opinions and 938 anonymous voters

In the Twitter poll below, Bill James posits about the role luck should play in our analysis. The 938 responses to that poll are almost evenly divided, suggesting many fall on both sides of the argument.

Bill James Poll on luck





Luck is a subjective way of saying that an unpredictable or random event has either turned your way or against you. In baseball you know non-RBI are going to happen, but you can’t predict when, nor how many times they will. More significantly perhaps, you can’t predict how many decisions will they affect.

Non-RBI are part of the game because they also count to determine the winner, they can be measured and are also lucky runs because you can’t predict them and because officially they were not runs created by the offense.

Methodology for calculating a lucky run

As mentioned, lucky runs are the difference between Scored Runs and RBIs and there were 1,024 of these in 2017. These 1,024 runs were recorded in only 797 of the 2,360 games played (33.8 %), so a third of MLB games in 2017 were affected by one or more lucky runs.

When does a lucky run become a lucky win?

This needs a deeper exploration and handling of the data than the previous case. With game data from retrosheets.com all games were analyzed using the following methodology:

A team was credited with a lucky win if the number of lucky runs it benefitted from was greater than or equal to the margin of victory. For example: If the final game score is 4 to 5 where the home team had 2 lucky runs, then it’s win was considered lucky.

On the other hand, on August 4th 2017, New York Yankees lost to Cleveland Indians 2 to 7 in a game where the Indians scored 3 lucky runs. However, because the number of lucky runs was less than the margin of victory, it was not considered a lucky win.

What can you do with the new concept?

The scope of this paper was to determine and measure what is a lucky run and when you can say that a team won a game by luck.

Also, this could be used to build, verify, or fine-tune any descriptive formulas or equations in runs created or adjusted models by excluding lucky runs since no player created them.

Finally, we should consider using a new equation for runs scored that can be written as Runs Scored = RBIs + non-RBIs.

Table 1 and which was the luckiest team in 2017

Table 1 is self-explanatory. Lucky runs per team is the difference between their scored runs and RBIs. It also includes the number of lucky wins calculated using the methodology described above as well as the percentage of wins determined to be lucky. The luckiest team was the Toronto Blue Jays with 10.53% of its wins resulting from lucky runs; 8 of their 76 wins were considered lucky. The least lucky team was the Detroit Tigers. At 1.56 %, only one win of the Tigers’ 64 were considered lucky.

Table 1

Team Scored Runs RBI Lucky Runs Lucky Wins 2017 Wins Lucky Runs /

Lucky Wins Lucky Wins /

Total Wins % Totals 22,582 21,558 1,024 117 Arizona Diamondbacks 812 776 36 4 93 9.0 4.30 Atlanta Braves 732 706 26 3 72 8.7 4.17 Baltimore Orioles 743 713 30 2 75 15.0 2.67 Boston Red Sox 785 735 50 4 93 12.5 4.30 Chicago Cubs 822 785 37 7 92 5.3 7.61 Chicago White Sox 706 670 36 4 67 9.0 5.97 Cincinnati Reds 753 715 38 2 68 19.0 2.94 Cleveland Indians 818 780 38 5 102 7.6 4.90 Colorado Rockies 824 793 31 2 87 15.5 2.30 Detroit Tigers 735 699 36 1 64 36.0 1.56 Houston Astros 896 854 42 3 101 14.0 2.97 Kansas City Royals 702 660 42 5 80 8.4 6.25 Los Angeles Angels 710 678 32 7 80 4.6 8.75 Los Angeles Dodgers 770 730 40 4 104 10.0 3.85 Miami Marlins 778 743 35 3 77 11.7 3.90 Milwaukee Brewers 732 695 37 6 86 6.2 6.98 Minnesota Twins 815 781 34 3 85 11.3 3.53 NY Mets 735 713 22 2 70 11.0 2.86 NY Yankees 858 821 37 3 91 12.3 3.30 Oakland Athletics 739 708 31 2 75 15.5 2.67 Philadelphia Phillies 690 654 36 4 66 9.0 6.06 Pittsburgh Pirates 668 635 33 5 75 6.6 6.67 Saint Louis Cardinals 761 728 33 3 83 11.0 3.61 San Diego Padres 604 576 28 4 71 7.0 5.63 San Francisco Giants 639 612 27 4 64 6.8 6.25 Seattle Mariners 750 714 36 6 78 6.0 7.69 Tampa Bay Rays 694 671 23 3 80 7.7 3.75 Texas Rangers 799 756 43 5 78 8.6 6.41 Toronto Blue Jays 693 661 32 8 76 4.0 10.53 Washington Nationals 819 796 23 3 97 7.7 3.09

Source: retrosheet.com and own calculation

By: Javier Caceres