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Chance Favors the Prepared Mind


Hey, I Play Football Too PDF Print E-mail
Chance Favors the Prepared Mind
Written by Todd Zola   
Tuesday, 16 August 2011 00:00

My apologies in advance to those of you that do not play fantasy football, but as the title of today’s submission implies, I dabble in fantasy football and decided to spend some bandwidth on fantasy baseball’s cousin.  That said, my offerings are going to come from a more analytical perspective than mainstream fantasy football discussions.

Something that has always bothered me about fantasy football advice is there is a ton of “small sample size” analysis by the respected experts in the hobby.  I am not kidding about this, but I heard two different radio hosts allude to a single play in the first pre-season game as the reason they are adjusting the player’s rank.  Really?  REALLY???  One play?  In the first exhibition game?  I am not trying to say I know anything about evaluating football talent.  All I am saying is I know that one play in a game where teams are trying not to get hurt means diddly squat.  As an aside, I listen to a podcast (The Bob and Tom Show) where the “sports guy”, Chick McGee, has coined the best line about preseason football, “the games don’t count but the injuries do.”

Speaking of small sample analysis, especially when watching quarterback play, keep in mind teams are not using their standard blitz packages so their performance may be enhanced a bit.  Yes Colt McCoy, I am looking at you.

Here is something else that has always bothered me about fantasy football advice, specifically projections.  To me, they need to at minimum pass the sniff test when it comes to being logically correct.  Specifically, I am talking about the disconnect between QB and WR rankings.  Too often, I see a QB ranked really high but the WR low or a pair of WR very high but the QB low.  Of course, there are more pass-catchers than the two WR, but show me a top QB that does not have at least one top WR.  I am not implying that the projections need to be a sum-zero entity.  We struggle with this all the time in baseball, with the balance between global accuracy and team accuracy.  But my point is, I have often seen sets of rankings with QB and WR that just do not make sense.

Here is something coming from the valuation side of things.  Keep in mind that while things like 6 points per passing TD and PPR (points per reception) give more raw points to the respective players, all QB get 6 points and all WR get PPR, etc.  The baseline level of expected production is also raised.  What counts is the number of points a player produced over the last drafted player at each position.  In a twelve team, 1 QB league, the 12th QB contributes no useful points.  In a twelve team, 2 RB league, the 24th RB contributes no useful points.  So while I am not saying that rankings are not altered if passing TD are worth 6 points or the league is PPR, I have heard some analysts over emphasize the impact, especially with respect to quarterbacks in 6 point per TD leagues.

We’ll end on a note to those bridging into auction play in fantasy football, especially if you are a veteran if baseball auctions.  Take your sheet with dollar values, crumple it up and toss it out the window.  Well, maybe put it into your recycling bin.  Here’s the deal.  Values are even less useful than they are in baseball.  I would study the player pool and determine the position(s) you feel most comfortable with the back-end talent.  Then, identify the most reliable players at the top end of the other pools and go get a couple.  Do not worry about “paying too much”, there is no such thing in football.  Trust me, assuming you really know the inventory, you will be able to secure a bunch of cheap players.   As an example, personally, I see some serious value at the back end of the RB and TE pool.  So in an auction, I would pay for one of the top QB and top WR.  I would not target just one and go get him, I would set up a group and try to get one as cheaply as possible within that group, but I would not use a price list as a boundary.  I would then get a back end RB as my RB1 and use the spaghetti method for my RB2 – throw a bunch of names against the wall and see what sticks.  With so many teams employing running back by committee, there should be some tasty options to emerge as the season progresses.

Last Updated on Tuesday, 16 August 2011 04:12
 
Calculating Rest of the Season Ratios PDF Print E-mail
Chance Favors the Prepared Mind
Written by Todd Zola   
Tuesday, 09 August 2011 00:00

As has been discussed a few times, it is a misconception that it is nearly impossible to gain or lose points in the ration categories (batting average, on base percentage, ERA and WHIP).  I will not regurgitate the arguments, but the primary point is everything is dependent upon the distribution in your league’s standings.  However, knowing you can make up the ground and actually doing it are two separate things.  Something I have found useful in my efforts to gain points in the ratio categories is having a target ratio in mind that is necessary to get the job done.  I can then look at the challenge objectively and determine if I truly have a chance at attain that target ratio.  So today’s submission is a downloadable Excel spreadsheet programmed to calculate the target ratio necessary to gain the potential points.

All you need is some general data usually available from your on-line scoring service.  To determine the target ERA and WHIP, all you need is the present number of innings pitched your staff has accrued along with your current ERA and WHIP.  Then all you need to do is estimate the number of innings you expect your staff to throw the remainder of the season and determine your season-ending target, usually based on your league’ standings and how many places you want to gain.   You enter these numbers and voila, the tool will compute the ERA and WHIP your staff needs to attain the rest of the season to finish at your target.

Something I like to do is take my present innings and prorate that total to determine an expected number remaining and set the target ERA and WHIP to see what results.  I then ask myself a series of questions to set my strategy.  Is the ERA and WHIP realistic?  If it is not, and I humbly feel there is no chance I can reach them, I consider lowering the innings and using solid middle relievers instead, assuming I can afford the hit in wins and especially strikeouts.  The idea here is replacing lower end starts with solid set up men lowers the likely rest of the year ERA.  Similarly, I will check out the wins and strikeouts to determine if I need to increase my starting pitchers to make up points in those categories, then estimate how that is apt to impact the ratios.   Instead of entering a target ERA and WHIP, I enter the ratio I expect my amplified staff to achieve to see if I lose too many points in ERA and WHIP.  The possibilities are endless.  For what it is worth, if your league uses a different ratio like K/9, it will still work just fine.

Also included is a similar tool to compute batting average, on base percentage or whatever hitting ratio your league uses.  All you need is the year to date at bats or plate appearances along with your current batting average or on base percentage.

Click HERE to download the ratio tool.

Last Updated on Tuesday, 09 August 2011 04:51
 
A Look at the July Pitching Numbers PDF Print E-mail
Chance Favors the Prepared Mind
Written by Todd Zola   
Tuesday, 02 August 2011 00:16

Another month has passed so it is time to continue our look at the pitching peripherals.  Obviously, offense remains down, but some suggested as the weather warms, the runs will pick up.  Check local listings, but suffice it to say the weather was certainly warm in July.

With respect to fantasy analysis, not much can be done now to game the results.   The off season is the time to investigate if there are trends that can be applied to projection theory.  But, this does serve as an avenue to remind everyone of something I have been preaching since the spring.  In fantasy terms, value is relative.  The 50th best pitcher may have superior stats than in the past, but he still impacts your team as much as the 50th best pitcher of previous seasons.  His ERA and WHIP may be lower, but so are the totals at each point in the standings.  The leaders in ERA and WHIP have lower numbers than in past seasons.   Okay, I think there is ample text to get us below the pictured advertisement; here is the monthly 2011 data to date in tabular form along with the same numbers since 2007.

2011

ERA

WHIP

K/9

BB/9

HR/9

BAbip

April/March

3.903

1.311

7.09

3.28

0.91

0.290

May

3.801

1.316

6.92

3.23

0.87

0.292

June

3.780

1.293

6.91

3.01

0.88

0.291

July

4.002

1.322

7.23

3.03

0.91

0.299

2010

ERA

WHIP

K/9

BB/9

HR/9

BAbip

April/March

4.199

1.379

7.13

3.66

0.95

0.296

May

4.174

1.365

7.00

3.40

0.94

0.298

June

4.137

1.362

6.97

3.17

0.93

0.303

July

4.100

1.343

7.03

3.12

1.03

0.297

August

4.012

1.327

7.21

3.10

0.97

0.297

Sept/Oct

3.894

1.314

7.40

3.27

0.95

0.291

2009

ERA

WHIP

K/9

BB/9

HR/9

BAbip

April/March

4.583

1.440

6.96

3.87

1.07

0.299

May

4.354

1.395

6.88

3.47

1.01

0.300

June

4.045

1.342

6.81

3.35

1.05

0.289

July

4.265

1.373

6.98

3.35

1.04

0.299

August

4.532

1.406

7.12

3.29

1.17

0.306

Sept/Oct

4.205

1.391

7.14

3.49

0.97

0.302

2008

ERA

WHIP

K/9

BB/9

HR/9

BAbip

April/March

4.165

1.388

6.41

3.64

0.90

0.291

May

4.135

1.370

6.82

3.32

0.97

0.298

June

4.179

1.380

6.81

3.32

1.07

0.297

July

4.583

1.395

6.93

3.23

1.09

0.304

August

4.407

1.403

6.92

3.34

1.05

0.305

Sept/Oct

4.511

1.412

7.10

3.48

1.00

0.306

2007

ERA

WHIP

K/9

BB/9

HR/9

BAbip

April/March

4.123

1.369

6.60

3.52

0.92

0.291

May

4.375

1.378

6.39

3.25

0.99

0.297

June

4.512

1.405

6.70

3.24

1.05

0.304

July

4.497

1.415

6.50

3.31

0.99

0.305

August

4.596

1.419

6.78

3.26

1.11

0.307

Sept/Oct

4.699

1.445

7.05

3.44

1.10

0.313

 

Even though the overall ERA is highest in July, in terms of skills, pitching actually improved this past month.  The increase in runs is due to a higher BABIP, resulting in more hits.  Hurlers in fact fanned more and walked fewer batters that earlier in the season.  Note that homers also remained consistent.  There have been some recent instances of a power spike in July.

As alluded to, it is too early to determine why pitching remains improved, but at least in terms of the root skills (K/9, BB/9, HR/9), the Year of the Pitcher continues.  Could it be that it is poorer hitting as opposed to better pitching?  Sure, especially since there can be a cause and effect dynamic happening.  Teams feel they need better defense and they better defenders are weaker hitters.  Obviously, PEDs cannot be ruled out.  But again, this is a chore for the off season to help hone projection accuracy.

For now, the take home message is the top power hitters are even more valuable than before as they can really impact the delta in the standings since the hitting categories are so tightly bunched.  That is, as we have discussed previously, depressed offense results in compressed categorical distribution, meaning the top players can really move the needle, depending of course on the gaps in your unique league.

Last Updated on Tuesday, 02 August 2011 10:39
 
Debunking the Myth of the Second Half Player PDF Print E-mail
Chance Favors the Prepared Mind
Written by Todd Zola   
Tuesday, 19 July 2011 00:21

One of the favorite discussion points at this time of the season is what players historically have strong second halves and are therefore acquisition targets and the flip-side, what players usually crash and burn so it is best to get rid of them.  This is a concept that has long bothered me as it is my belief that while examples of such players likely exist, the fact they have done it for a few years is not predictive of it continuing to happen.  That is, I do not feel it is salient analysis to label a player as “first half” or “second half” and strategize accordingly.

The example I have used in the last involves the flipping of a coin.  My argument is if 32 people flip a coin five times, probability dictates that one will flip five heads.  One person flipping five heads is therefore expected within the range of possibilities.  I use this metaphor for the fantasy baseball population.  There are 750 active players at a given time and well over 1200 that are active over the course of the season.  Based on sheer randomness, one of 32 could have a better first (or second) half five years in a row.  That is somewhere between 25 and 35 players which is a pretty decent amount.  And no doubt, the list fantasy enthusiasts come up with each season are replete with these 25 to 35 players.  If you want to extend the metaphor further, one of 64 players will repeat their first or second half performance six consecutive campaigns.  So, I would even contend that if a player displayed a trend for six or even seven years, it fell within the realm of statistically anticipated outcomes.

The thing is, this sort of analysis is done on players who fared better the second half of LAST season, let alone two or three seasons.  If the analysis is spotty over multiple years, it is certainly suspect based on three months of data.  Again, I am not contending such a player does not exist.  There very well may be players that for one reason or another take a few months to get it going or peter out at the end.  All I am saying is it is not sage to look at what Gordon Beckham or Stephen Drew did the second half of last season and target them now, just as it would be a bad idea to get rid of Dan Haren because he always collapses in the second half.

A year or so ago, I made this point in the forums at our friends at Baseball HQ and a couple of guys much smarter than me in this area had their interest piqued and did some of their own analysis.  The forum is private so I cannot share every intimate detail, but using Bayesian probability analysis (Google it, I had to),  they concluded that for at least an individual season, some first and second half splits feel outside of the expected range of performance level.  The player studied was Adam LaRoche, long considered a second half monster.  The study did not perfectly address my specific hypothesis, but it was enlightening in that for at least one season, it could be strongly argued that LaRoche was statistically better in the second half and it was not just a case of skills not translating into results, which is so often the case in these instances.

I will conclude this discussion by doing something I often frown upon and deem hack analysis, and that is providing selected anecdotal examples to illustrate my point.  The difference is others cite a couple of examples as proof of their argument.  I only want to highlight some rather well known players as a means to help convince those not believing the argument that I at least could be correct, as well as point out how some of this due to perception and not reality in terms of skills not wavering, but rather surface stats not being reflective of said skills.

The first player that piqued my personal interest in this realm was Ichiro Suzuki.  Those of you that have played this game for several seasons might recall that when he first came over to the States, Ichiro enjoyed better first halves, thus the smarts always advised getting rid of him at the All Star break or thereabouts.  Here are his first half and second half numbers, using March to June and July to Oct as the cutoffs.

2001

HR

R

RBI

SB

AVG

1H

3

70

36

27

0.349

2H

5

57

33

29

0.350

2002

HR

R

RBI

SB

AVG

1H

2

62

28

21

0.359

2H

6

49

23

10

0.286

2003

HR

R

RBI

SB

AVG

1H

7

59

26

21

0.340

2H

6

52

36

13

0.284

2004

HR

R

RBI

SB

AVG

1H

3

39

29

19

0.315

2H

5

62

31

17

0.423

2005

HR

R

RBI

SB

AVG

1H

6

51

27

18

0.294

2H

9

60

41

15

0.312

2006

HR

R

RBI

SB

AVG

1H

4

61

27

25

0.350

2H

5

49

22

20

0.295

2007

HR

R

RBI

SB

AVG

1H

5

56

39

23

0.368

2H

1

55

29

14

0.336

2008

HR

R

RBI

SB

AVG

1H

3

57

21

33

0.293

2H

3

46

21

10

0.328

2009

HR

R

RBI

SB

AVG

1H

6

38

18

16

0.373

2H

5

50

28

10

0.333

2010

HR

R

RBI

SB

AVG

1H

3

31

24

21

0.333

2H

3

43

19

21

0.299

 

So in his rookie campaign, Ichiro did not display any difference.  But in his second and third seasons, he indeed had markedly better first halves.  I distinctly recall the pundits suggesting he be dealt before the crash and burn in 2004.  Why you might ask?  Simple – I looked at his first half and determined he was snake-bit and was more than willing to take advantage of others hastiness and acquired him everywhere I could, and 2004 was a very good year for me.  Keep in mind, BABIP had yet to become a household acronym.  My point is, I was more focused on what was happening in 2004, and not at all what transpired the latter part of the two previous seasons.  I felt a low BABIP with a still-stellar contact rate was a more reliable indicator of future performance, in this of the improved variety than the fact Ichiro struggled the second half of ’02 and ’03.

The next guy that got my dander up when he was constantly called a second half pitcher was Johan Santana.  What bothered me was his skills remained consistent one half to the next, but his surface stats, most notably ERA happened to be better for a few second halves.  My argument at the time was it is not a sure thing that if you acquire Santana at the break, your ERA and WHIP would benefit.  Let’s take a look at some numbers:

2004

ERA

WHIP

K/9

BB/9

HR/9

BABIP

1H

4.3782

1.1959

9.12

2.28

1.46

0.297

2H

1.2526

0.7114

11.48

2.02

0.56

0.208

2005

ERA

WHIP

K/9

BB/9

HR/9

BABIP

1H

3.7768

0.9732

10.53

1.69

1.04

0.280

2H

2.0308

0.9694

8.05

1.81

0.68

0.253

2006

ERA

WHIP

K/9

BB/9

HR/9

BABIP

1H

2.586

0.9634

9.43

1.52

0.91

0.274

2H

2.9654

1.0318

9.44

2.11

0.94

0.272

2007

ERA

WHIP

K/9

BB/9

HR/9

BABIP

1H

2.7632

1.0439

9.47

2.21

1.26

0.267

2H

3.9429

1.1048

9.86

2.06

1.46

0.283

2008

ERA

WHIP

K/9

BB/9

HR/9

BABIP

1H

3.0087

1.2228

8.16

2.53

1.11

0.292

2H

2.0885

1.0774

7.68

2.31

0.67

0.265

 

In 2004, Santana’s first year as a full-time starter, he had a markedly batter second half.  His skills were improved across the board after June.  However, there was also some good fortune involved as his BABIP was quite lucky, leading to more opportunities to pitch from the more comfortable wind-up which may have assisted in the better peripherals.

In 2005, the southpaw’s ERA was better in the second half, but his skills were actually better in the first half, at least in terms of strikeouts and walks.  What improved was he kept the ball in the yard post break.  While this could be part skill, it is also part good fortune.  But, since this was now two straight season of a better ERA in the second half, Santana was labeled as someone to go get for the second half.

But alas, look what happened in 2006.  He was the basically the same pitcher both halves, though walking a couple more hitters leading to a slightly elevated ERA and WHIP.  The key is he did not have a BETTER second half as many that season anticipated.   Knowing many would want to potentially overpay for Santana after the break, I drafted him in a couple of trading leagues and indeed got a king’s ransom for him later.  I also had a pretty good 2006.

Real quickly, in 2007, Santana had a much better ERA in the first half, but the difference in skills was not sufficient to account for the disparity, he was a bit unlucky the second half.  Then is 2008, his second half looked better but was actually just a tad luckier.

The bottom line is these were two famous examples of the herd feeling a player’s second half fate could be anticipated based a two year trend.  But that is all it was, a trend, not a pattern.  If someone in your league is willing to overpay for your Mark Teixeira because he is a second half stud, oblige them.  If Dan Haren’s owner is looking to rid their staff of the impending struggles, help ease their mind and open your arms to the Angel’s ace.  You will not regret it.  Well, at least you shouldn’t anyway.

Last Updated on Tuesday, 19 July 2011 08:40
 
First Half All-Profit Team PDF Print E-mail
Chance Favors the Prepared Mind
Written by Todd Zola   
Tuesday, 05 July 2011 00:21

Today we are going to take a look at the All-Value team based on first half performance.  What I did was take the National Fantasy Baseball Championship ADP and assign dollar values per draft spot based on history.  I opted to use the NFBC ADP as opposed to our pre-season projections as a “wisdom of the crowd” exercise.  You will not find a sharper group of drafters anywhere and the sample is sufficient to weed out those using odd strategies that could skew an ADP.  I kept the analysis simple, just calculating the amount the players earned over what was expected based on their draft spot.  All reserves were assigned an expected value of $0.

While it is interesting, not to mention fun to draw conclusions from the results to aid in future drafts, it must be noted that we are only halfway through the season, the trends may change, not to mention there is no guarantee next year’s player pool acts in the same manner as 2011.  That said, there are some trends I am going to keep my eye on with the hope it gives me an edge come next spring.

Here is your FIRST TEAM ALL-PROFIT FIRST HALF SQUAD:

Player

POS

ADP

Actual

Expected

Profit

Asdrubal Cabrera

SS

251.45

$30

$5

$25

Curtis Granderson

OF

104.81

$40

$15

$25

Lance Berkman

1B

250.74

$29

$5

$24

Matt Kemp

OF

19.8

$49

$28

$21

James Shields

SP

200.34

$28

$8

$20

David Ortiz

UT

177.54

$27

$9

$18

Michael Morse

OF

275.52

$21

$4

$17

Alex Gordon

OF

277.77

$21

$4

$17

Jhonny Peralta

MI

247.18

$22

$5

$17

Carlos Beltran

OF

225.63

$22

$7

$16

Jose Bautista

3B

31.52

$39

$24

$15

Paul Konerko

CI

72.07

$33

$19

$15

Michael Pineda

SP

264.31

$19

$4

$15

Justin Verlander

SP

47.37

$36

$22

$15

Danny Espinosa

2B

236.37

$20

$6

$14

Jair Jurrjens

SP

279.9

$17

$4

$13

Jered Weaver

SP

60.28

$31

$20

$11

Miguel Olivo

C

288.93

$14

$3

$11

Anibal Sanchez

SP

250.21

$16

$5

$11

Josh Beckett

SP

166.78

$20

$10

$10

Kyle Farnsworth

CL

314.09

$11

$2

$9

Joel Hanrahan

CL

180.45

$17

$9

$8

Russell Martin

C

253.25

$13

$5

$8

 

And your SECOND TEAM ALL-PROFIT FIRST HALF SQUAD:

Player

POS

ADP

Actual

Expected

Profit

Michael Bourn

OF

132.98

$28

$13

$15

Johnny Damon

OF

310.98

$17

$2

$15

Ryan Ludwick

OF

315.46

$15

$1

$14

Jose Reyes

SS

25.79

$40

$27

$14

Seth Smith

OF

299.08

$16

$2

$14

Erick Aybar

MI

277.18

$17

$4

$13

Jacoby Ellsbury

OF

42.2

$34

$22

$12

Adam Lind

CI

152.7

$23

$11

$12

Gaby Sanchez

1B

180.51

$21

$9

$12

Placido Polanco

3B

316.28

$12

$1

$11

Yunel Escobar

UT

253.98

$16

$5

$11

Ty Wigginton

2B

279.5

$13

$4

$9

Ian Kennedy

SP

198.92

$17

$8

$9

Ricky Romero

SP

188.17

$16

$8

$8

Scott Baker

SP

259.91

$12

$4

$8

Johnny Cueto

SP

275.02

$12

$4

$8

Ryan Madson

CL

310.99

$10

$2

$8

Craig Kimbrel

CL

163.02

$18

$10

$8

Paul Maholm

SP

578.76

$7

$0

$7

Cole Hamels

SP

57.75

$27

$20

$7

Tim Stauffer

SP

274.6

$10

$4

$6

J.P. Arencibia

C

251.37

$11

$5

$6

Jason Varitek

C

484.55

$5

$0

$5

 

With the caveat that the following are presently observations and do not yet have any tangible application to game theory, here are some quick thoughts:

1.  I am pleased by the fact that there are not as many pitchers at the upper end of each profit list.  One of the dictums most preach is value pitching always emerges.  At least through the first half of the season, my preseason suggestion that as a populace, we are getting better at valuing pitching may have legs.  Though, we do need to keep in mind the specific populace used here is the NFBC drafter, but, so far, so good.  It will be interesting to determine at season’s end if this pattern still holds.

2.  It is curious that there are no catchers that are significantly outperforming their expectations.  I’m not sure yet how to apply this, but it is worth noting.

3.  Something not shown form the above data is the vast majority of the next group of high profit players are outfielders.  This lends credence to notion of leaving a couple of outfield spots available to be considered fungible, in search of one or two of these value players.

We will broach this again at season’s end.  Good luck to everyone, here is hoping you have a bunch of top second half profit earners on your squad(s)!  Later this week, we will take a look at those on the opposite end of the spectrum, those earning the most negative value the first three months.

Last Updated on Tuesday, 05 July 2011 01:12
 
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