Category Archives: Outside Articles

Cubs barely survive a bullpen meltdown, but they have a Craig Kimbrel problem

That’s a disaster appearance in every sense of the term, and unfortunately for the Cubs it’s roughly in keeping with Kimbrel’s recent history. At his peak, he was on the short list of the most dominant closers in baseball history, but that peak is fading deeper and deeper into the past. Kimbrel showed signs of eroding command during the 2018 season for the Red Sox, even though he wound up with strong numbers overall.

Source: Cubs barely survive a bullpen meltdown, but they have a Craig Kimbrel problem – CBSSports.com

It appears Craig Kimbrel is under a microscope again after yesterday when  he “earned” himself a Hold stat by giving up 2 runs and 4 walks.  Cubs signed him last season because their relief staff needed help and they were very much contenders for NL Central in June.

This article makes some broad declarations so let’s look at Craig Kimbrel.  Here’s his career according to this data model.

Craig Kimbrel

Year Rank WAA TeamID Pos
2010 +189+ 1.76 ATL PITCH
2011 +082+ 3.17 ATL PITCH
2012 +039+ 4.41 ATL PITCH
2013 +042+ 4.18 ATL PITCH
2014 +087+ 3.07 ATL PITCH
2015 +165+ 1.93 SDN PITCH
2016 XXXXX 0.99 BOS PITCH
2017 +041+ 4.49 BOS PITCH
2018 +163+ 1.99 BOS PITCH
2019 XXXXX -0.97 CHN PITCH

An excellent career with 5 years in top 100 and only not ranked twice over his 10 years in the league.   Last season was his first below average year after missing the first two months according to this data model.   Cubs acquired Kimbrel to help relief staff instead he dragged it down further.

Craig Kimbrel WAR

Year Rank WAR TeamID Pos
2010 XXXXX 0.7 ATL PITCH
2011 +173+ 2.4 ATL PITCH
2012 +099+ 3.3 ATL PITCH
2013 +110+ 3.3 ATL PITCH
2014 +157+ 2.5 ATL PITCH
2015 XXXXX 1.2 SDN PITCH
2016 XXXXX 0.9 BOS PITCH
2017 +082+ 3.6 BOS PITCH
2018 +180+ 2.3 BOS PITCH
2019 -140- -0.5 CHN PITCH
TOTAL X 19.7

As has been shown here many times WAR under values relief and here’s another example.  Kimbrel barely makes top 100 in 2012 and 2017 and out of the top 200 4 times according to WAR.  Both WAR and this data model generally agree on his career trajectory and his values for the year 2017 and 2018.

In 2012, one of Kimbrel’s career years helping Atlanta win 94 games WAR ranks him #99.  Darwin Barney in 2012, playing 2B for the Cubs that won only 61 games, ranked #34 in MLB by WAR.  Was Darwin Barney better than Kimbrel that year?

Player acquisition is always a gamble, sometimes more sometimes less.  Cubs needed help in relief, Kimbrel was available, and they took a chance.  It didn’t work out and Cubs didn’t make the playoffs.  Had it worked out Cubs may have just barely made the playoffs but would most likely lose in the first round.  It takes a team effort to win and a team effort to lose.

Modeling player contracts requires very complex financial math and risk analysis far outside the scope of this data model.  If this season continues on, and there doesn’t seem to be any drama today on the Twitter, then we’ll be able to have a better look see into Cubs relief situation for next year.  This is just one bad outing for Kimbrel in a very unusual season.  Tomorrow is another day.

Vegas has Cubs almost even steven with Reds for today’s game.  Not sure why.  More on this later.  Until then ….

MLB Rejects MLBPA Proposal; No Counter-Offer Planned

Owners contend that ommissioner Rob Manfred can seek to unilaterally impose a shortened season if the union won’t budge from its prorated salary demands, and it appears that’s where they’re leaning, per the New York Post’s Joel Sherman. Either a 48- to 54-game season with fully prorated salaries or an 82-game season at less than prorated salaries are under consideration.

Source: MLB Rejects MLBPA Proposal; No Counter-Offer Planned – MLB Trade Rumors

A 54 game season is essentially 2 months of baseball.  The below teams highlighted in red and blue would be playoff eligible at the end of 2 months of play last season.

MLB 20190531

Tm W L BAT PITCH UR  
MIN 38 18 64.1 35.7 5.4
LAN 39 19 48.0 42.7 -4.6
NYA 37 19 28.6 41.7 2.4
HOU 38 20 32.5 53.7 8.4
TBA 35 20 -8.5 88.7 4.4
PHI 33 24 17.0 9.7 3.4
CHN 31 24 21.0 31.7 -3.6
MIL 32 26 24.5 -10.3 3.4
SDN 30 27 -38.5 23.7 1.4
ATL 30 27 7.6 0.7 -7.6
COL 29 27 40.5 -42.3 9.4
OAK 30 28 17.6 3.7 8.4
TEX 28 27 44.1 -25.3 -0.6
BOS 29 28 33.5 -4.3 -2.6
SLN 28 28 4.5 5.7 2.4
PIT 28 28 -31.9 -24.3 -5.6
NYN 28 29 -1.8 -10.3 -0.6
CLE 28 29 -43.4 25.7 -4.6
CHA 28 29 -19.5 -21.3 -1.6
ARI 28 30 32.5 -0.3 5.4
CIN 27 30 -7.5 40.7 10.4
ANA 27 30 12.0 -31.3 11.4
WAS 24 33 1.0 -26.3 -1.6
SEA 25 35 38.5 -59.3 -34.6
DET 22 33 -73.5 -11.3 -5.6
SFN 22 34 -56.0 -18.3 -11.6
TOR 21 36 -48.4 -5.3 -4.6
MIA 19 36 -92.0 2.7 7.4
KCA 19 38 -11.5 -41.3 9.4
BAL 18 39 -35.4 -71.3 -4.6

Good news for Cubs, bad news the Nationals and Cardinals.  With so few games the field will be tight which may result in many 3 way or more ties.  A baseball season is a 6 month marathon that consumes summer with a fall finale.  A two month season is like running 9 miles and calling that a marathon.  Washington had a real team WAA of -9 at the end of two months of play  which wasn’t good.  They had plenty of time to recover adding +6 WAA each of the next 4 months; gaining momentum that propelled them through playoff season.  Gaining +6 for a full 6 months puts a team at 99 wins at the end of a season.

After only 54 games this model may be some use in handicapping playoffs but only as a guide. 

The Fall Of The Freak

Of course, Lincecum brought a superb resume to the Angels. As a member of the Giants from 2007-15, “The Freak” made four All-Star teams, won three World Series championships and took home two National League Cy Young Awards. The San Francisco version of Lincecum also piled up 1,643 2/3 regular-season innings (269 appearances, 261 starts) and posted a 3.61 ERA with 9.33 K/9 and 3.54 BB/9. For the most part, Lincecum’s career started going off the rails in 2012, in which his 2.74 ERA from the prior season skyrocketed to 5.18, but there was still some magic left. Lincecum threw a 148-pitch no-hitter against the Padres in 2013, and he no-hit the Friars yet again the next season.

Source: The Fall Of The Freak – MLB Trade Rumors

Above is an example how sports writers in general explain a career. Here’s how this data model explains a career.

Tim Lincecum

Year Rank WAA TeamID Pos
2007 XXXXX 1.55 SFN PITCH
2008 +005+ 9.09 SFN PITCH
2009 +004+ 9.64 SFN PITCH
2010 +101+ 2.96 SFN PITCH
2011 +022+ 5.86 SFN PITCH
2012 -011- -5.12 SFN PITCH
2013 -091- -2.25 SFN PITCH
2014 -027- -3.59 SFN PITCH
2015 XXXXX -0.29 SFN PITCH
2016 -014- -4.39 ANA PITCH
TOTAL X 13.46 PITCH

Easy to see which years he won Cy Young awards and which years he made all star team.  His career did go off the rails in 2012 and stayed off the rails.

Hard to work on this data model without a live stream of data we get during a baseball season.  This live stream of data is invaluable for finding bugs in the code and useful new features.  In the meantime still working on converting this perl code into php and then java.  More on this later ….

Cubs lose 5th straight 1-run game

On Sunday, they became just the second team in 100 years to get swept at home in a four game series and lose all four by a single run. They also became the first major league team since 2011 to lose five straight one-run games. It’s the first time it’s happened to the franchise since 1915.

Source: Crumbling Cubs lose 5th straight 1-run game

There are three color coded assertions above.  Let’s look at all three starting with the assertion colored in blue. After hearing about this 5 game streak it occurred that a question like this is something this data model should easily handle.  This however required modifying a script that counts stuff in historical game logs since I hadn’t envisaged this use case.

First things first.  What is the probability of losing 5 one run games in a row?  Since we have no other information other than there are two outcomes to each event.   We can assume

P(lose) = 1/2 = P(win) , just like a flip of a coin.

The probability of a one run game is 0.30 using data from 1970 – 2018 , thus

P(one run game) = 3/10.

The probability of losing a one run game is P(L) * P(one run game) = 0.5 * 0.3 = 0.15.  This is the same as the probability of winning a one run game.  Thus, the probability of losing 5 one run games in a row would be 0.15^5 which is around 1/13169.

Since there are 157 * 30 / 2 = 2355 possible starts to a 5 game series that means we should expect one occurrence every 6 or 7 seasons.  Between May 8 and May 13 Arizona lost 5 straight 1 run games which is around what we would expect   Before that in September 1988, 23 seasons before 2011,  Atlanta lost 6 one run games in a row which is a larger gap than we would expect.  Distributions are never perfect — especially with small sample sizes.

Let’s look at the second assertion colored in brown. Each team plays around 11 four game home stands per season or a little over half of their 81 home games.  In 100 years that would be around 1000 events where a sweep like that can happen.

P(losing 4 one run games in a row) = [P(Lose) * P(one run game)]^4 = 0.15^4 = 1/1975

Probability of going so long without losing 4 one run games in a home stand after 1000 events or 100 years is around 60%.   The probability of it not happening next season is around 99.44%.  In other words, this assertion has nothing to do with the quality of the Cubs as a team and more how the pachinko ball bounces.

Update 10/1/2019:  In other words, had the Cubs lost a game by 2 runs in the middle of that losing streak noone would be talking about this.  Whether a team loses by 2 runs or 1 run is irrelevant but streaks make for click bait and give sportscasters something to pontificate about.

The Cubs went 2-7 in the last 10 days of the season losing 5 games.  How many a team loses in a row or how they lose those games is irrelevant.  Had they went 7-2 instead there would have been a 3 way scrum like last year for two playoff spots and who knows how that would have turned out.

Bottom line:  A baseball season is a marathon and the final record of a team encompasses 162 games played over 6 months — not a mere 5 in less than a week.  From all 10 parts to the Playoff Horse Race series of posts here it was clear very early Cubs didn’t have the horses to win an NL pennant let alone a World Series this year.  Cubs remained stagnant albeit above average all season so they ended the season about where they should have.

End of Update

And finally, for the assertion in green.  1915 is 104 years of baseball or around 16,000 games played.  We saw above that the probability of losing 5 one run games in a row is 1/13169.  The probability of going 16,000 games without losing 5 one run games in a row is

P’ = ( 1 – (1/131619 ) ) ^ 16000 =~ 30%

Thus it was a 70% possibility of it happening again in the time frame since 1915.

The probability of it not happening next season is 99.8%.  What does this have to do with the current Cubs team?  Nothing. Cubs simply couldn’t pull off wins at the end of this season and sometimes numbers align funny.

Wild Card handicapping and playoff coverage starting tomorrow.  Until then ….

DISCLAIMER: There are probably one or more errors in the math above.

5 storylines to follow the remainder of the Iowa Cubs season

Iowa hasn’t been to the playoffs since 2008. But that could change this season. Iowa finished the first half of the season with a 52-38 mark, tops in the Pacific Coast League’s American Northern Division. It owns a 10-game lead over the second place Omaha Storm Chasers.

Source: 5 storylines to follow the remainder of the Iowa Cubs season

Our analysis of this Iowa Cubs team here.