Saturday, August 6, 2022

The Dodgers have had at least a .100 OPS differential in each of the last 5 years and most likely will make it 6 this year yet no other team has ever done more than 3 years in a row since 1901

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:

  1. Does OPS have a direct correlation with wins?

    ReplyDelete
  2. 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.

    ReplyDelete