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Chance Favors the Prepared Mind


The All-Time Draft PDF Print E-mail
Chance Favors the Prepared Mind
Written by Todd Zola   
Wednesday, 26 October 2011 00:00

What do high stakes fantasy baseball players do during the World Series?  A bunch of us are participating in “The All Time Draft.”  Over at the National Fantasy Baseball Championship (NFBC) forum, we are having a contest where the winner gets an entry into next spring’s NFBC Classic.  The league uses standard 5x5 scoring with the normal 23 roster spots.  The player pool is composed of everyone in Major League Baseball, starting in 1901.  But here is the catch – you name the player and declare the season and that player and all other seasons are now off the board.  If the player appeared for 20 games at more than one position, you can move him between positions, but once you declare a season, you are locked into that year.  Another nuance is we are going to draft selected rounds “blind”.  That is, we are all going to send our picks for rounds 5, 10, 15 and 23 to an impartial third party.  At the end of each blind round, the names of the 15 players will be revealed, but not the owner or the season.  The purpose for this was originally to take the pressure off someone with one of the last few picks who was not going to win, but could in essence control who did.  While this is still a primary reason, it was also decided that this would add a shroud of mystery to the draft to retain interest as well as add the wrinkle that while we have a feel for what each other is doing, there is a bit of uncertainty.

I was invited to participate in the league provided I serve as the spreadsheet bitch, tracking the standings and rosters.  Fortunately, I already had an Excel program designed that could handle the festivities so I gladly accepted the invitation and began plotting my strategy.  The first step was learning our draft position.  The NFBC utilizes a concept they call the Kentucky Derby System (KDS), where you put in order your preferred picks.  Names are drawn from a hat and the first name gets their top choice.  This is the means horses and jockeys choose their gate in the Kentucky Derby, hence the name.  I ended up smack dab in the middle, pick eight.  I figured just like in a regular draft, the advantage of this spot is having the best chance not to miss out on a run with the disadvantage being not really able to initiate a run or strategically pair up picks.

While I readily admit my fantasy baseball life is an open book for anyone motivated enough to glean how I feel about the player pool and my favored strategies, I apologize but I am going to be a bit coy about my plan for this unique draft.  After all, a free NFBC entry is on the line and these guys are good.  That said, I have no qualms sharing my ranking system, but first I need to talk a little about rankings in general.

It is human nature to want to put a static number next to each player and say “Smith is better than Jones.”  If there was a single number that we could attach to each player that would accurately measure his value, we could in fact do this.  But due to the rotisserie scoring system and hitting and pitching dynamic, this is not possible.   Actually, the ratio nature of batting average, ERA and WHIP alone render it impossible to use a static number since ultimately, the value of the ratio stat depends on the number of at-bats or innings, which is not known until the end of the draft.  It should be noted that this is not referring to the excess statistics you have over team directly below you in each category – there is no accounting for this.  The chief single number is really not an accurate means to rank the players as the individual category rankings depend upon one’s strategy and that strategy may very well change as a result of the flow of the draft as well as the strategy of one’s opponents and how that impacts the player pool.   By means of example, if you decide to ignore or at minimum, not focus on a category like stolen bases, your value for steals is different than everyone else.  In a league of this nature, this could even mean you not only value a player higher or lower than someone else, you value individual seasons from the same player differently.  This phenomenon adds to the intrigue and attraction to this sort of exercise.

With that as a backdrop, here is a quick review of how I prepared my rankings to use as a guide.  For hitters, to help keep my spreadsheet to a reasonable size, I only downloaded seasons of more than 450 at-bats, which I bet will turn out to be low, I could have set the filter higher but I wanted to make sure I captured seasons of hitters that walk a lot.  For pitching, I only included seasons of 50 or more innings pitched, then did a quick filter to get rid of seasons with fewer than 150 innings with no saves.  Again, the only reason for doing this was to keep the spreadsheet to a size that was manageable.

The next step was converting the ratios to a counting stat.  I used the same formula I use for dollar value calculations, comparing the player’s stat to a baseline stat then multiplying by the at-bats or innings.  As suggested above, the setting of the baseline impacts the counting stat conversion, adding a degree of error to the number.  I chose a batting average, ERA and WHIP worse than I expect the last place team in the respective categories to finish, minimizing the number of negative points earned.

Next, I set up a worksheet for each category and sorted by the roto stat I was scoring that time.  Let us use homers as an example.  Since there will be 210 hitters drafted, I found the top 210 home run seasons after deleting the non-leading seasons for every player.  In other words, 210 different players comprise this list – it is not the top 210 home run seasons.  I then made sure there were ample players of each position to fill the 15 rosters legally.  Next, the total of these 210 stats was determined.  The category value of each player was computed as (player stat) / (total of the pool) x 1000.  I chose 1000 arbitrarily; it was selected because the final ranking of each player was a number easy to eyeball -not too big and not too small.  This was done for all five categories, hitters and pitchers.

Long time readers of this space know I like to consider value over replacement and that is the next step.  This is the same as value based drafting in football, you find the last player drafted at each position and subtract that number of points from everyone at the position.  Middle infield, corner infield and utility add a little twist, which is part of the strategy I am going to omit since I believe it may end up to be integral to the final results (more on that later).  The larger point is at the end of the day, I had a ranking sheet with exactly enough players to fill all 15 rosters in a legal manner.  It must be reiterated that these are solely values in a vacuum and depending on my ultimate strategy, the relative positions could change.  That said, the fact that I used 1000 to normalize each category gives a major hint to my initial plan – BALANCE.  Others are going to bulk up in some categories in lieu of others.  At least initially, I want to give myself a chance to compete across the board.

The draft is underway and as mentioned, I am reticent to provide my complete thought process for each pick until the draft has concluded, so I apologize for that.  With my first pick, I took Ty Cobb’s 1911 season which caused a slight stir since he only hit 8 homers, putting me way behind those who took a hitter first.  But, I picked up 83 steals with a .420 average, but the best part was 147 runs and especially 127 RBI.  Think about the high speed, low power players of today and find me one with that level of runs and RBI.  Basically, because picking this season from the Georgia Peach will only put me behind in one category, I was willing to take the plunge feeling I can make up for lost power later, especially with a .420 average to use as a buffer.   Next, I went with the 1997 campaign of Mike Piazza, adding 40 homers, 104 runs and 124 RBI.  Without giving away too much, I did not have to jump Piazza up on my list because he is a catcher; he was my top non-outfielder hitter on the board at the time.

To whet your appetite, here are the first two rounds:

1.01 1921 Babe Ruth-OF
1.02 1908 Ed Walsh-P
1.03 1922 Rogers Hornsby-2B
1.04 1913 Walter Johnson-P
1.05 1908 Christy Mathewson-P
1.06 1904 Jack Chesbro-P
1.07 1915 Pete Alexander-P
1.08 1911 Ty Cobb-OF
1.09 1965 Sandy Koufax-P
1.10 1904 Joe McGinnity-P
1.11 1904 Rube Waddell-P
1.12 1931 Lou Gehrig-1B
1.13 1901 Nap Lajoie-2B
1.14 1968 Bob Gibson-P
1.15 1997 Larry Walker-OF

2.15 1932 Jimmie Foxx-1B
2.14 1930 Hack Wilson-OF
2.13 2001 Alex Rodriguez –SS
2.12 1930 Chuck Klein-OF
2.11 1976 Joe Morgan-2B
2.10 1909 Mordecai Brown-P
2.09 1930 Al Simmons-OF
2.08 1997 Mike Piazza-C
2.07 1998 Sammy Sosa-OF
2.06 2001 Barry Bonds-OF
2.05 1937 Joe DiMaggio-OF
2.04 1912 Smoky Joe Wood-P
2.03 1920 George Sisler-1B
2.02 1910 Jack Coombs-P
2.01 1930 Babe Herman-OF

In general, my belief is the winner of this unique setup is going to be the one who has a solid set of relative rankings, but more importantly, does the best job of gauging the flow of the draft to best know when to take hitting and when to take pitching as well as factoring in positional and categorical aspects.  In other words, pretty much the same as regular drafts.

I am sure I will be providing updates on this draft from time to time, but please feel free to follow and even comment on the NFBC forums.

Last Updated on Wednesday, 26 October 2011 09:01
 
A Look at Consistency in Fantasy Football PDF Print E-mail
Chance Favors the Prepared Mind
Written by Todd Zola   
Tuesday, 30 August 2011 00:59

I apologize, my intent was to post this at the end of last week, but as I was crunching the data, the project took on a mind of its own and I kept thinking of better ways to do it, which led to more questions to answer and before I knew it, the weekend was upon me and I was in the dark for a day, literally, as Irene rendered me powerless all day Sunday.  The irony is that even after spending extra time on the project, I am still in the dark.  But, I decided to present what I have done and see where it takes us.

One of the more popular notions that has evolved in fantasy football circles the past several seasons is that a consistent team is preferred over one that one week kicks tail and the next scuffles.  Some analysts have developed metrics to gauge the consistency.  I admit, I should have done some more due diligence before embarking on this study, reading up on exactly what others have done, but alas, I did not.  I just read and hear consistency alluded to in a lot of fantasy advice so I thought I would take a look at the principle.

Intuitively, I can make a strong argument that consistency is better.  Using the guise that you want to win, the assumption is your team scores better than the league average.  I mean, who cares if it better to score the league average number of points by being consistent or having some great weeks mixed in with poor ones.  Regardless of the schedule, unless you are extremely lucky, a squad averaging the league number of points is going to be a .500 team, give or take a little.  Anyway, as just alluded to, the schedule and luck of the draw plays a major role in fantasy football.  Without looking at the data like we will do soon, my gut tells me there is something to the consistency concept.  By consistently scoring above the league average, this forces your opponent --EVERY WEEK -- to score even higher than the league average.  Sure, the luck of the draw is still a factor, but your score is fixed and only teams that do really well will defeat you.  If your seasonal average is ten percent above the league average but you have some really strong weeks and some really week ones, you are opening yourself up to losing to a team that scores at or below league average that week as well as scoring a ton of wasted points during a matchup you would have one by scoring considerably fewer.  Since I am someone that prefers data over my gut, no matter how large it may be, I thought this would make a neat little study.

I chose the weekly score to be investigated to be ten percent above the league average.   My data showed that a team averaging ten percent above the league average would finish 9-5 or 10-4 in a 14-game fantasy season.  This seemed quite reasonable.  Enter my first conundrum.  Should I do the study with the team scoring ten percent each week’s league average, or use a fixed number equal to ten percent more than the average score of all fourteen weeks?  I opted to do both, though my lean is to favor the former, assuming ten percent above each individual week’s average.

I used three private leagues as my model.  They are all twelve team leagues but use different scoring systems so the league averages are all different.  The sample of three leagues is way too small to make any definitive conclusion, but my thinking was the impact of consistency may differ depending on the average points scored in each league, perhaps based on the scoring system.  Usually, the lower the points scored, the fewer points are assigned to yardage bonus meaning touchdowns are paramount.   The point being, the consistency of players scoring touchdowns and getting yardage points could be different.  Are you sort of getting where I was coming from in the introduction?  The more I got into it, the more I realized the question was not so black and white; there is some serious gray area.

The study was comparing the appropriate points scored total to what actually occurred, and determining the corresponding winning percentage.  You then compare all the different winning percentages to examine the impact of consistency.

What I will do is slowly walk you through one league, presenting the data along the way, then present the data for the other two leagues.  I will begin with assuming that the team averages ten percent above the league total each week.

In the first league I studied, the average for all teams was 84.3 points.  A team scoring exactly the league average each week had a .504 winning percentage.  A team scoring ten percent above each weekly average was .649 (about a 9-5 record).  This is the most consistent team possible and their yearly average was 91.7 points.  What I then did was access varying degrees of consistency.  I used four percent increments, adding and subtracting that percentage from the team’s “consistent” score, which was itself ten percent above the weekly average.  I determined how a team scoring that percent above and below would fare in terms of winning percentage.  I looked at +/- 4%, 8%, 12%, 16% and 20%.  In each case, the team’s yearly average is the same; the assumption is in half the weeks the team did better, the other half it did worse.  Here are the associated winning percentages:

0%

0.649

4%

0.646

8%

0.646

12%

0.637

16%

0.643

20%

0.631

 

This data indeed suggests there is something to consistency.  Remember, in each of those instances, the total number of points the team scored is exactly the same.

These results led me to a question.  Would it be better to field a consistent team that averaged fewer points than this team?  To look at this, I subtracted 2%, 4%, 6% and 8% from the team’s weekly totals.  Keep in mind that s subtracting 10% results in a team scoring the league average every week.  Here is a table showing how a team consistently scoring that number of would fare.  The average is included to give a perspective to the numbers, remember that this study uses +/- each week’s average.  That is, the number provided is the yearly average.

0%

83.4

0.506

2%

86.7

0.530

4%

85.1

0.565

6%

88.4

0.607

8%

90.1

0.631

10%

91.7

0.649

 

At least for this league, a consistent team scoring fewer points was not better than a more inconsistent one averaging more points.  Hang on, you’ll see why this is relevant in a bit, don’t forget, we have two more leagues to look at.

Here is the data for this league assuming that the team scores the same number of points each week, exactly ten percent above the seasonal team average:

0%

0.667

4%

0.670

8%

0.646

12%

0.634

16%

0.649

20%

0.631

 

Here, the slightly inconsistent team actually fared a smidge better, but obviously not enough to be significant.  And now, here is a look at consistent teams scoring a little less than ten percent above the seasonal average each week:

0%

83.4

0.524

2%

86.7

0.554

4%

85.1

0.601

6%

88.4

0.619

8%

90.1

0.637

10%

91.7

0.667

 

Here, a consistent team scoring a couple fewer points is about the same as a more inconsistent team scoring a few more points.

I will now present the data for the second league.  This one averaged 126.9 points per team, so the study was done on a team averaging ten percent more, or 139.6.

0%

0.708

4%

0.682

8%

0.667

12%

0.664

16%

0.649

20%

0.619

 

Chalk another one up for consistency.  Here are the results of the team scoring incrementally fewer points:

0%

126.9

0.494

2%

132.0

0.536

4%

129.4

0.565

6%

134.5

0.607

8%

137.1

0.667

10%

139.6

0.708

 

A slightly worse (in terms of average points) but more consistent team can hang with a slightly inconsistent team but has a better record than a more inconsistent squad scoring more points.

And here is the data, side by side, for a team averaging the same number of points every week:

0%

0.667


0%

126.9

0.524

4%

0.667


2%

132.0

0.565

8%

0.682


4%

129.4

0.607

12%

0.673


6%

134.5

0.613

16%

0.649


8%

137.1

0.643

20%

0.628


10%

139.6

0.667

 

Now the third league, with the team scoring ten percent (146.5) above the league average (133.3), side by side:

0%

0.690


0%

133.2

0.536

4%

0.679


2%

138.5

0.565

8%

0.682


4%

135.8

0.583

12%

0.661


6%

141.1

0.631

16%

0.631


8%

143.8

0.679

20%

0.604


10%

146.5

0.690

 

The trends are similar.  The first set of data shows a consistent team scoring the same number of points as an inconsistent team is desired.  The chart on the right suggests that a team scoring even fewer points, on a relative basis, than the first two leagues is still better than an inconsistent teal scoring more points.  There is actually a trend forming here, but the sample is too small to draw any concrete conclusions, but at least with respect to these three leagues, the more points the league scored, the more you want to be consistent since you can average even fewer points and be better than a consistent team scoring more.

And finally, the data from the third league with the team averaging the same total every week:

0%

0.708


0%

133.2

0.494

4%

0.682


2%

138.5

0.536

8%

0.667


4%

135.8

0.565

12%

0.664


6%

141.1

0.607

16%

0.649


8%

143.8

0.667

20%

0.619


10%

146.5

0.708

 

Are you still with me?  If so, thanks.  So we have presented a ton of numbers, probably in a confusing manner only to corroborate what was initially assumed in the first place.  But like I said, it is always nice when data supports intuition.  That said, I do think the study needs to be expanded.  Even in a small sample, I see enough to believe in the consistency factor.  My problem is, I am not quite sure how to incorporate this into drafting.  Do you forgo upside for a player with higher floor?  Do you build a consistent foundation then take your chances?  The same thing goes when setting your lineup.  Do you opt for the player that is more likely to score a moderate number of points or the one that can go off any week, but can also be invisible?  All this bandwidth and we end up with more questions than answers.

Thus ends my foray into the realm of fantasy football.  Thanks for indulging me and good luck with your drafts!!

 

Last Updated on Tuesday, 30 August 2011 02:10
 
Fantasy Football: It Is All About the Points per Game PDF Print E-mail
Chance Favors the Prepared Mind
Written by Todd Zola   
Tuesday, 23 August 2011 00:00

Last week, I took a look at a concept known as value based drafting.  While the proper conclusion would have been drawn either way, in it, I made a rather significant mistake when it comes to more useful fantasy football rankings.  The basis for my calculation was the total points scored by each player.  The more useful metric to investigate is points per game.

While the head-to-head format is growing in fantasy baseball, it is the stalwart in football.  The majority of baseball leagues are rotisserie scoring based, and thus only care about the season-long totals of the pertinent statistics.  It is a bit more difficult to account for injury replacement as most leagues are weekly formats where players will miss portions of weeks as opposed to football, where he either plays that week or he does not.  Thus, since it is quite easy to compute projected points per game in football, this is a much better measure of what is expected when it comes to setting your draft lists.  The same value based calculation that was discussed last week can be utilized with points per game as the component ranked.

The following are the final point totals and point per game totals derived from a private league I run.  The columns to focus upon are the last two as these rank first total points, then points per game.  I used eight games played as the cut-off.

QUARTERBACKS

Player

Points

PPG

Games

Points rank

PPG rank

Vick, Michael PHI QB

374.5

31.208

12

4

1

Rodgers, Aaron GBP QB

383.7

25.58

15

1

2

Rivers, Philip SDC QB

383.4

23.962

16

2

3

Manning, Peyton IND QB

381.8

23.862

16

3

4

Brees, Drew NOS QB

373.8

23.363

16

5

5

Brady, Tom NEP QB

360.5

22.531

16

6

6

Roethlisberger, Ben PIT QB

269.8

22.483

12

19

7

Manning, Eli NYG QB

344

21.5

16

7

8

Orton, Kyle DEN QB

273

21

13

16

9

Garrard, David JAC QB

293.8

20.986

14

14

10

Fitzpatrick, Ryan BUF QB

272.7

20.977

13

17

11

Palmer, Carson CIN QB

321.8

20.112

16

8

12

Hill, Shaun DET QB

221

20.091

11

24

13

Schaub, Matt HOU QB

319.85

19.991

16

9

14

Freeman, Josh TBB QB

314.95

19.684

16

10

15

Cutler, Jay CHI QB

295

19.667

15

13

16

Ryan, Matt ATL QB

313.75

19.609

16

11

17

Kitna, Jon DAL QB

211.25

19.205

11

26

18

Flacco, Joe BAL QB

304.2

19.013

16

12

19

Cassel, Matt KCC QB

284.3

18.953

15

15

20

 

RUNNING BACKS

Player

Points

PPG

Games

Points rank

PPG rank

Foster, Arian HOU RB

401.8

25.113

16

1

1

McFadden, Darren OAK RB

291.4

22.415

13

5

2

McCoy, LeSean PHI RB

305.2

20.347

15

2

3

Hillis, Peyton CLE RB

305.05

19.066

16

3

4

Peterson, Adrian MIN RB

284.3

18.953

15

6

5

Gore, Frank SFO RB

206.5

18.773

11

18

6

Charles, Jamaal KCC RB

292.5

18.281

16

4

7

Johnson, Chris TEN RB

282.9

17.681

16

7

8

Forte, Matt CHI RB

280.6

17.538

16

8

9

Rice, Ray BAL RB

276.9

17.306

16

9

10

Jones-Drew, Maurice JAC RB

240.1

17.15

14

13

11

Bradshaw, Ahmad NYG RB

252.4

15.775

16

10

12

Mendenhall, Rashard PIT RB

251.1

15.694

16

11

13

Moreno, Knowshon DEN RB

200.1

15.392

13

19

14

Jackson, Steven STL RB

245

15.312

16

12

15

Tomlinson, LaDainian NYJ RB

216.2

14.413

15

15

16

Turner, Michael ATL RB

230

14.375

16

14

17

Best, Jahvid DET RB

211

14.067

15

16

18

Benson, Cedric CIN RB

206.9

12.931

16

17

19

Tolbert, Mike SDC RB

188.3

12.553

15

23

20

 

WIDE RECEIVERS

Player

Points

PPG

Games

Points rank

PPG rank

White, Roddy ATL WR

318.2

19.887

16

1

1

Johnson, Andre HOU WR

256.6

19.738

13

8

2

Nicks, Hakeem NYG WR

250.2

19.246

13

9

3

Bowe, Dwayne KCC WR

290.6

18.163

16

2

4

Johnson, Calvin DET WR

272.2

18.147

15

6

5

Lloyd, Brandon DEN WR

287.8

17.988

16

3

6

Jennings, Greg GBP WR

286.5

17.906

16

4

7

Wayne, Reggie IND WR

282.5

17.656

16

5

8

Wallace, Mike PIT WR

267.7

16.731

16

7

9

Owens, Terrell CIN WR

230.3

16.45

14

17

10

Jackson, DeSean PHI WR

229

16.357

14

18

11

Harvin, Percy MIN WR

219

15.643

14

21

12

Witten, Jason DAL TE

250.2

15.637

16

9

13

Johnson, Steve BUF WR

249.3

15.581

16

11

14

Colston, Marques NOS WR

228.3

15.22

15

19

15

Austin, Miles DAL WR

243.3

15.206

16

12

16

Fitzgerald, Larry ARI WR

241.7

15.106

16

13

17

Moss, Santana WAS WR

241

15.062

16

14

18

Maclin, Jeremy PHI WR

236

14.75

16

15

19

Marshall, Brandon MIA WR

205.7

14.693

14

24

20

 

The important thing is not to focus on the specific players as much as it is to demonstrate that looking at PPG and not just points can alter the ranking of the player.  Of course, there is a little more to it than that as obviously, you will need to use an inferior player for the games your player is expected to miss.  We will get to that in a minute.

The take home lesson from this bit of analysis is if you are a person that crunches the numbers in fantasy football to come up with your draft list and not simply make intuitive rankings like so many, you must account for the number of games each player is expected to play.  If you use an outside source of projections, it is imperative that they include a games played column.  If not, you cannot really be sure what the prognosticator intended.  For an example, does anyone really expect Michael Vick to play a full season?  Most projections will be tempered to account for the injury factor.  To properly gauge Vick’s relative rank, you really need to know his PPG, not his projected points.

As suggested, an adjustment needs to be made if the player is anticipated to miss time.  One way to account for this is to estimate the PPG of the player you will use in his stead and determine the PPG via a weighted average.  By means of example, let us go back to Vick and last season’s numbers.  He averaged an impressive 31.2 in twelve games.  Ignoring the fact that most fantasy leagues have a regular season and playoffs and most do not play in Week 17, we need to account for the four missing games from Vick’s total.  The average PPG of the 13th- 24th ranked QB was 19.0, so a reasonable estimation would be for 19.0 points per game in the four missing games.  The adjusted calculation is

((12 x 31.2) + (4 x 19))/16 = 28.15

So let’s say that instead of the above being actual stats, they were in fact 2011 projections.  The 28.15 PPG would still put Vick as the top-rated QB, it would then be up to you to decide if the injury risk, and the “inconsistency” of the expected numbers are worth drafting. 

While I do not profess to be qualified to project football numbers, I am comfortable suggesting that even without the health concerns, Vick’s PPG in the games he plays is likely to drop from last season, so when you do the injury adjustment, you will be forced to choose between Vick’s adjusted PPG and someone else’s similar PPG but who is expected to play the full season like Tom Brady, Drew Brees, Aaron Rodgers or Philip Rivers.  For those of you trying to determine how far to drop Payton Manning, you may want to run this calculation with the anticipation he misses a game or two to see where the numbers fall.  A similar treatment can be given to Chris Johnson if you want to account for his potential of missing games due to his holdout.

On Thursday, we will conclude this foray into fantasy football with an examination of the notion that consistency trumps upside when it comes to designing a roster.

Last Updated on Tuesday, 23 August 2011 20:31
 
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
 
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