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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