The table below shows all the teams since 1901 that had an OPS differential of at least .100 since 1901. The teams that did it 3 years in a row are in red and the 2017-2021 Dodgers are in blue.
This year the Dodgers hitters have an OPS of .779 while their pitchers have allowed .629, for a .150 differential.
| Team | Year | HOPS | POPS | Diff | W-L% | 
| ATL | 1994 | 0.767 | 0.667 | 0.100 | 0.596 | 
| ATL | 1997 | 0.769 | 0.655 | 0.114 | 0.623 | 
| ATL | 1998 | 0.795 | 0.657 | 0.138 | 0.654 | 
| BAL | 1969 | 0.756 | 0.620 | 0.136 | 0.673 | 
| BOS | 1912 | 0.735 | 0.628 | 0.107 | 0.691 | 
| BOS | 2003 | 0.851 | 0.742 | 0.109 | 0.586 | 
| BOS | 2004 | 0.832 | 0.727 | 0.105 | 0.605 | 
| BOS | 2007 | 0.806 | 0.705 | 0.101 | 0.593 | 
| BRO | 1941 | 0.752 | 0.641 | 0.111 | 0.649 | 
| BRO | 1953 | 0.840 | 0.722 | 0.118 | 0.682 | 
| CHC | 1906 | 0.671 | 0.536 | 0.135 | 0.762 | 
| CHC | 1910 | 0.711 | 0.611 | 0.100 | 0.675 | 
| CHC | 2016 | 0.772 | 0.633 | 0.139 | 0.640 | 
| CHW | 1994 | 0.810 | 0.708 | 0.102 | 0.593 | 
| CLE | 1920 | 0.793 | 0.690 | 0.103 | 0.636 | 
| CLE | 1948 | 0.792 | 0.665 | 0.127 | 0.626 | 
| CLE | 1954 | 0.744 | 0.626 | 0.118 | 0.721 | 
| CLE | 1995 | 0.839 | 0.718 | 0.121 | 0.694 | 
| CLE | 2017 | 0.788 | 0.673 | 0.115 | 0.630 | 
| DET | 1984 | 0.774 | 0.674 | 0.100 | 0.642 | 
| HOU | 2017 | 0.823 | 0.720 | 0.103 | 0.623 | 
| HOU | 2018 | 0.754 | 0.640 | 0.114 | 0.636 | 
| HOU | 2019 | 0.848 | 0.681 | 0.167 | 0.660 | 
| LAD | 1974 | 0.743 | 0.634 | 0.109 | 0.630 | 
| LAD | 2017 | 0.771 | 0.671 | 0.100 | 0.642 | 
| LAD | 2018 | 0.774 | 0.669 | 0.105 | 0.564 | 
| LAD | 2019 | 0.810 | 0.661 | 0.149 | 0.654 | 
| LAD | 2020 | 0.821 | 0.627 | 0.194 | 0.717 | 
| LAD | 2021 | 0.759 | 0.624 | 0.135 | 0.654 | 
| NYG | 1904 | 0.673 | 0.573 | 0.100 | 0.697 | 
| NYG | 1905 | 0.718 | 0.576 | 0.142 | 0.686 | 
| NYG | 1911 | 0.748 | 0.637 | 0.111 | 0.647 | 
| NYM | 1988 | 0.721 | 0.620 | 0.101 | 0.625 | 
| NYY | 1921 | 0.838 | 0.725 | 0.113 | 0.641 | 
| NYY | 1927 | 0.873 | 0.676 | 0.197 | 0.714 | 
| NYY | 1931 | 0.840 | 0.725 | 0.115 | 0.614 | 
| NYY | 1932 | 0.830 | 0.714 | 0.116 | 0.695 | 
| NYY | 1934 | 0.782 | 0.682 | 0.100 | 0.610 | 
| NYY | 1936 | 0.865 | 0.733 | 0.132 | 0.667 | 
| NYY | 1937 | 0.825 | 0.704 | 0.121 | 0.662 | 
| NYY | 1939 | 0.825 | 0.667 | 0.158 | 0.702 | 
| NYY | 1942 | 0.740 | 0.639 | 0.101 | 0.669 | 
| NYY | 1998 | 0.825 | 0.699 | 0.126 | 0.704 | 
| NYY | 2002 | 0.809 | 0.705 | 0.104 | 0.640 | 
| NYY | 2009 | 0.839 | 0.734 | 0.105 | 0.636 | 
| NYY | 2017 | 0.785 | 0.680 | 0.105 | 0.562 | 
| PHA | 1909 | 0.664 | 0.561 | 0.103 | 0.621 | 
| PHA | 1910 | 0.684 | 0.573 | 0.111 | 0.680 | 
| PHA | 1928 | 0.799 | 0.685 | 0.114 | 0.641 | 
| PHA | 1929 | 0.816 | 0.692 | 0.124 | 0.693 | 
| PHA | 1931 | 0.789 | 0.680 | 0.109 | 0.704 | 
| PIT | 1901 | 0.721 | 0.614 | 0.107 | 0.647 | 
| PIT | 1902 | 0.719 | 0.570 | 0.149 | 0.739 | 
| PIT | 1903 | 0.735 | 0.630 | 0.105 | 0.650 | 
| SDP | 2020 | 0.798 | 0.689 | 0.109 | 0.617 | 
| SEA | 2001 | 0.805 | 0.679 | 0.126 | 0.716 | 
| SFG | 2021 | 0.769 | 0.658 | 0.111 | 0.66 | 
| SLB | 1922 | 0.823 | 0.717 | 0.106 | 0.604 | 
| STL | 1942 | 0.717 | 0.590 | 0.127 | 0.688 | 
| STL | 1943 | 0.725 | 0.611 | 0.114 | 0.682 | 
| STL | 1944 | 0.745 | 0.615 | 0.130 | 0.682 | 
| TEX | 2011 | 0.800 | 0.698 | 0.102 | 0.593 | 
 
2 comments:
Does OPS have a direct correlation with wins?
Here is a post I did several years ago
https://cybermetric.blogspot.com/2014/10/the-relationship-between-ops.html
The The r-squared was .869 in one regression, meaning that 86.9% of the variation in winning pct is explained by OPS differential. The correlation is .932 and that squared is .869. So their is a high correlation between ops differential and winning pct
I used regression analysis to see how big the impact of OPS differential was on winning (using the years 2010-2014).
Here, instead of using individual years, I used the average OPS differential and average winning pct for all 30 teams over the last 5 years.
The regression equation from using individual years was
Pct = 1.325*OPSDIFF + .5
The r-squared was .827 and the standard error was .029. Over 162 games, that is 4.639 wins
The regression equation from using the 5 year average was
Pct = 1.3465*OPSDIFF + .5
The r-squared was .869 and the standard error was .017. Over 162 games, that is 2.72 wins. That is a big drop from the first regression. In a given year, luck will play a role. But the more seasons and data that are used the more accurate the relationship. By combining the years, some of the good and bad luck evens out.
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