With a quarter of the season in the books, time to review

From one of the coolest baseball sites out there coolstandings.com we are beginning to see how the MLB races might shape up.


from coolstandings.com
http://www.coolstandings.com/baseball_standings.asp?i=1

2013 AL Standings What are these?
East	W	L	PCT	GB	RS	RA	EXPW	EXPL	DIV	WC	POFF
New York	27	16	.628	-	184	159	92.7	69.3	34.2	26.4	60.6
Boston	27	17	.614	0.5	217	179	93.2	68.8	35.5	27.0	62.5
Tampa Bay	23	20	.535	4	210	197	87.4	74.6	17.9	24.0	41.9
Baltimore	23	20	.535	4	213	195	84.3	77.7	12.0	19.4	31.4
Toronto	17	26	.395	10	177	222	67.2	94.8	0.4	1.7	2.1
Central	W	L	PCT	GB	RS	RA	EXPW	EXPL	DIV	WC	POFF
Cleveland	25	17	.595	-	208	173	92.6	69.4	39.0	22.0	61.0
Detroit	23	19	.548	2	222	175	93.0	69.0	40.7	21.7	62.4
Kansas City	20	20	.500	4	171	157	83.0	79.0	13.0	15.5	28.5
Chicago	19	23	.452	6	155	170	74.8	87.2	3.7	6.3	10.0
Minnesota	18	22	.450	6	180	191	74.5	87.5	3.5	6.2	9.8
West	W	L	PCT	GB	RS	RA	EXPW	EXPL	DIV	WC	POFF
Texas	29	15	.659	-	212	158	100.9	61.1	83.1	5.7	88.8
Oakland	23	22	.511	6.5	207	204	82.1	79.9	11.1	14.0	25.2
Seattle	20	24	.455	9	160	180	76.3	85.7	4.2	7.4	11.6
Los Angeles	17	27	.386	12	189	227	70.3	91.7	1.5	2.7	4.2
Houston	12	32	.273	17	173	261	54.0	108.0	<0 .1="" td="">	<0 .1="" td="">	<0 .1="" td="">

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2013 NL Standings
East	W	L	PCT	GB	RS	RA	EXPW	EXPL	DIV	WC	POFF
Atlanta	25	18	.581	-	186	152	97.0	65.0	76.2	6.9	83.0
Washington	23	21	.523	2.5	155	172	81.5	80.5	14.3	13.9	28.2
Philadelphia	21	23	.477	4.5	156	190	74.7	87.3	6.0	5.8	11.8
New York	17	24	.415	7	174	202	70.8	91.2	3.5	3.4	6.9
Miami	12	32	.273	13.5	117	190	51.7	110.3	<0 .1="" td="">	<0 .1="" td="">	<0 .1="" td="">
Central	W	L	PCT	GB	RS	RA	EXPW	EXPL	DIV	WC	POFF
St. Louis	28	15	.651	-	201	150	98.2	63.8	50.6	30.5	81.1
Cincinnati	26	18	.591	2.5	210	161	94.0	68.0	32.6	36.7	69.3
Pittsburgh	26	18	.591	2.5	176	157	86.9	75.1	14.2	30.0	44.2
Chicago	18	25	.419	10	173	176	74.1	87.9	1.9	8.2	10.1
Milwaukee	17	25	.405	10.5	177	207	69.3	92.7	0.6	3.7	4.3
West	W	L	PCT	GB	RS	RA	EXPW	EXPL	DIV	WC	POFF
Arizona	25	19	.568	-	180	155	89.5	72.5	37.1	18.2	55.3
Colorado	24	20	.545	1	221	189	89.6	72.4	37.5	18.3	55.7
San Francisco	24	20	.545	1	202	207	82.8	79.2	16.7	14.7	31.3
San Diego	20	23	.465	4.5	176	188	76.6	85.4	7.4	8.2	15.6
Los Angeles	17	25	.405	7	142	180	66.9	95.1	1.3	1.7	3.1

Baseball statistics

x: Clinched Division   y: Clinched Wild card W: Wins  L: Losses  PCT: Winning percentage GB: Games Back (Number of games behind the Division leader) RS: Runs Scored  RA: Runs Against

Baseball "coolstats"   What are these?

EXP W: Expected season Wins  EXP L: Expected season Losses DIV: % chance of winning the Division  WC: % chance of winning the Wild Card POFF: % chance of making the Playoffs

In the AL it's back to the Yankees and the Sawks, with the Rays and the Orioles battling to see who will be the 3rd wheel. Toronto invested miserably in the ex-Marlins (TREND ALERT) and are about to have an early fork stuck in them.

The Tigers and Indians are battling it out for the AL Central with the Royals battling the Chi-Sox for third wheel status. The Twins have fallen on hard times.

In the AL West the Rangers have adjusted to life without Josh rather nicely and Oakland is doing it's usual thing. The Angels invested lavishly, but not well (2ND REFERENCE). The Astros are battling to not get relegated to AAA.

In the NL East, the Braves are threatening to run and hide from the Nats. The Phillies are battling the Mets for most mediocre status and the Marlins should be relegated to AAA status.

In the NL Central, the Cardinals are doing what the Cardinals always do, WIN. The Reds and Pirates are battling to stay in view. The Cubs and the Brewers are AAAA teams at best right now.

In the NL West. the D-Backs, Rockies and Giants are playing footsie with the 1-3 spots. San Diego and the Dodgers are battling for the basement and the Dodgers are furiously calculating how they could spend so lavishly and get so little in return (3RD REFERENCE). I guess even with all the Harvard MBA's and SABR-metricians on staff that Magic's money can buy, a negative Return on Investment (ROI) is possible.

You can put that in your SABR pipe and smoke it. Round two goes to the Hawk-eroo.

TWTW!! Maybe Hawk Harrelson has it right

After listening to the recent debate between the Hawk and ESPN's Brian Kenney (YouTube below), I couldn't help but think that maybe the Hawk has a point, maybe two. Whether you call it TWTW or GRIT or whatever, intangibles are a quality that by definition do not lend themselves to being quantified by definition, but you know them when you see them.

from thefreedictionary.com:
http://www.thefreedictionary.com/intangible

3.	intangible - hard to pin down or identify; "an intangible feeling of impending disaster" unidentifiable - impossible to identify

Some guys have that quality of making the other guys around them better. They make the total of the team greater than the sum of its parts. They are leaders and chemistry builders. And some guys are the opposite.

Winners have the intangibles!!! And like many things, you don't appreciate what you've got until after it's gone. Then you realize the hard to quantify value-added that these guys provide after they are missing from the equation. Then the team is equal to ( mediocrity ) or maybe even less than the sum of its parts ( under-achievers ).  Of course, then it's too late.

That's what Hawk is trying to explain to the geek squad here, IMO.

from The Big Lead:

http://www.thebiglead.com/index.php/2013/04/27/hawk-harrelson-invented-a-new-sabermetric-category-twtw-the-will-to-win-video/ The first example that came to my mind was Tim Tebow in 2011 with Denver. Artistically, not a QB work of art. Statistically, not a work of art. But his teams win consistently. It took the Jets to break the streak and that says more about the Jets than it does Tebow.  The Jets have been a cesspool of bad personnel decisions and Tebow was supposed to come in and be the team plunger how? By holding a clipboard? Anyway, I thought I would take a look at the 2011 year in quarterbacking in the NFL and began with the premise th at the better the QB statistically, the better the teams record would be. So,  I wanted to see the correlation between the ESPN total QB rating and winning. In addition, I wanted to see if there were any examples like Tebow of QB's who violated the statistical model, so to speak. Who won sometimes in spite of their liabilities as a QB as defined by the statistical metric. They were "bad" QB's who just won ball games. Here is the metric and how they determine the QB rating. There were some interesting results (shown below). from ESPN: http://espn.go.com/nfl/qbr/_/year/2011 Glossary * Season Leaders: On pace for 250 action plays. * All-time data reflects 2008 onwards. PASS EPA: Clutch-weighted expected points added on plays with pass attempts. RUN EPA: Clutch-weighted expected points added through scrambles, designed rushes and fumbles/fumble returns on running plays. SACK EPA: Clutch-weighted expected points added (lost) on sacks. PEN EPA: Clutch-weighted expected points added on penalties. TOTAL EPA: Total clutch-weighted expected points added. ACT PLAYS: Plays on which the QB has a non-zero expected points contribution. Includes most plays that are not handoffs. QB PAR: Number of points contributed by a quarterback over the season, accounting for QBR and how much he plays, above the level of a quarterback who plays very rarely and is on the fringe of the NFL. QB PAA: Number of points contributed by a quarterback over the season, accounting for QBR and how much he plays, above the level of an average quarterback. TOTAL QBR: Total Quarterback Rating, which values quarterback on all play types on a 0-to-100 scale. http://espn.go.com/nfl/story/_/id/6833215/explaining-statistics-total-quarterback-rating A quick primer on the fundamentals of Total Quarterback Rating: Scoring: 0-100, from low to high. An average QB would be at 50. Win Probability: All QB plays are scored based on how much they contribute to a win. By determining expected point totals for almost any situation, Total QBR is able to apply points to a quarterback based on every type of play he would be involved in. Dividing Credit: Total QBR factors in such things as overthrows, underthrows, yards after the catch and more to accurately determine how much a QB contributes to each play. Clutch Index: How critical a certain play is based on when it happens in a game is factored into the score. For the W-L data for each QB, I used CBS Sports NFL data. The W-L data includes only games where the QB started and includes playoff games if the team advanced that far. (TWTW) from CBSSports.com: http://www.cbssports.com/nfl/players The correlation between the Total QBR and the QB winning percentage (W_Pct) as a starter was 0.64. No surprise, the NFL is a QB driven league nowadays. Based on this small sample, the correlation number implies that about 40% of a teams winning percentage is derived from the play of their quarterback. The first thing that jumps out are the names at the top of the list are WINNERS. The creme de la creme of the NFL. Aaron Rogers, Drew Brees, Tom Brady, Tony Romo and Matt Ryan are the top five and win at about the rate that their stellar play implies The bottom of the list gives us Blaine Gabbert, Curtis Painter, Sam Bradford, Tim Tebow and Mark Sanchez. Painter and Gabbert won at about the level of play their QBR would indicate. Sam Bradford seemed to just have one of those miserable season where everything went wrong for him and his team. But Tebow and Sanchez both out kicked their coverage so to speak. Sanchez had a QBR of 33.6 and an W_Pct of 50.0% for a plus 16.40 score. Tebow came in with a 29.9 QBr and a W_Pct of 61.5% for a plus 31.64 score. The list of over-acheivers, possessing dare I say TWTW, were: John Skelton @ +39.90 Alex Smith @ +31.98 Tim Tebow @ +31.64 Mark Sanchez @ +16.40 Joe Flacco @ +12.52 Tom Brady @ +10.63 Jay Cutler @ +10.20 The interesting name on the list was Skelton who out-performed a more highly regarded QB in Kevin Kolb ( 34.4 QBR - 33.33 W_Pct) for the same Arizona Cardinal team. Apparently, Skelton may have had had that je ne sais quoi, the TWTW that Hawk is alluding to and he was able to rally the team around him as Tebow did the Broncos in relief of Kyle Orton. Skelton and Tebow are interesting because they took over the same team, with the same defensive squad and rallied them from depths to heights. That result defines what Hawk is speaking about. We're just not certain how to identify it in advance, but we can generally spot it after the fact pretty well. The rest of the overachiever names you can generally say had good to excellent defensive teams around them. How much of the teams performance can be attributed to good defense and how much of the defensive performance is aided by good QB play (keeping them off the field, playing from ahead more often than from behind, etc.) is fodder for another post. Hindsight and many forms of statistical analysis are always 20/20.  I think that is the Hawk's frustration with the SABR crowd and it is well placed in some instances. Looking at the other end of the scale, the under-achievers yields some interesting names. These guys QBR was significantly higher than their team record for whatever reason. Maybe they were stat gatherers and not team motivators. maybe they were on bad teams.  Kyle Orton @ -29.80 Tony Romo @ -21.40 Josh Freeman @ -18.63 Sam Bradford @ -18.60 Carson Palmer @ -18.16 Matt Schaub @ -17.50 Cam Newton @ -17.50 Some of these guys were on good teams, with good defenses, soooooooooo.......IDK. Lets just say I would score this one in favor of Hawk Harrelson over Brian Kenney and Harold Reynolds by a TKO. STOP THE FIGHT!! And they can roll their eyes and snicker amongst themselves all they want.
TWTW Total QBR W-L W_Pct W_Pct Diff Player 86.2    15-2 88.2%        88.24        2.04 Aaron Rodgers 84.0            14-4 77.8%        77.78       (6.22) Drew Brees 72.7       15-3 83.3%        83.33      10.63 Tom Brady 71.4      8-8 50.0%        50.00     (21.40) Tony Romo 69.1    10-6 62.5%        62.50       (6.60) Matt Ryan 67.5             5-5 50.0%        50.00     (17.50) Matt Schaub 65.5    10-6 62.5%        62.50       (3.00) Matt Stafford 64.4      7-6 53.8%        53.85     (10.55) Michael Vick 63.6    11-5 68.8%        68.75        5.15 Ben Roethlisberger 62.8      9-7 56.3%        56.25       (6.55) Matt Hasselbeck 62.7      8-8 50.0%        50.00     (12.70) Philip Rivers 62.6      4-5 44.4%        44.44     (18.16) Carson Palmer 59.8      7-3 70.0%        70.00      10.20 Jay Cutler 59.7    13-5 72.2%        72.22      12.52 Joe Flacco 59.4    12-7 54.5%        54.55       (4.85) Eli Manning 56.6      6-6 50.0%        50.00       (6.60) Matt Moore 55.0    6-10 37.5%        37.50     (17.50) Cam Newton 51.2      4-5 44.4%        44.44       (6.76) Matt Cassell 50.5    6-10 37.5%        37.50     (13.00) Ryan Fitzpatrick 49.8      1-4 20.0%        20.00     (29.80) Kyle Orton 45.8      9-7 56.3%        56.25      10.45 Andy Dalton 45.8    14-4 77.8%        77.78      31.98 Alex Smith 45.3    4-11 26.7%        26.67     (18.63) Josh Freeman 43.9      5-8 38.5%        38.46       (5.44) Rex Grossman 40.1      4-9 30.8%        30.77       (9.33) Colt McCoy 37.7      7-8 46.7%        46.67        8.97 Tarvaris Jackson 35.1      6-2 75.0%        75.00      39.90 John Skelton 34.4      3-6 33.3%        33.33       (1.07) Kevin Kolb 33.7      2-9 18.2%        18.18     (15.52) Christian Ponder 33.6      8-8 50.0%        50.00      16.40 Mark Sanchez 29.9      8-5 61.5%        61.54      31.64 Tim Tebow 28.6      1-9 10.0%        10.00     (18.60) Sam Bradford 22.5    2-12 14.3%        14.29       (8.21) Curtis Painter 20.6    4-11 26.7%        26.67        6.07 Blaine Gabbert 0.641328597   Correl OBR - W_Pct  GUILFORD’S SUGGESTED INTERPRETATION FOR CORRELATION COEFFICIENT VALUES Value  Interpretation Less than .20    Less than .20 Slight, almost negligible relationship .20 - .40  .20 - .40 Low correlation; definite but small relationship .40 - .70  .40 - .70 Moderate correlation; substantial relationship .70 - .90  .70 - .90 High correlation; marked relationship .90 - 1.00  .90 – 1.00 Very high correlation; very dependable relationship