Hoping we never have to play Illinois.

Poster format courtesy of Motivator

Can I Go Home Now?

We've all been there. The home team drops seven runs in an early inning. By the seventh inning stretch they've shown no sign of an offense, and are still six runs down. It's hot, muggy, Washington night, there are thunderstorms brewing over the horizon, and your wife just phoned that she heard on WTOP that Rt. 50 to Annapolis was closing for repair work at 10 p.m.

Question: If I go home now, am I likely to miss anything? Aside from heat stroke and road rage?

Answer: Probably not. If the visiting team is ahead by six runs after the top of the seventh, historically the chance that the home team will pull out a victory is 1.6%, or about 60 to 1 against. Unless you're of the extreme optimist persuasion I'd suggest going home.

I got to thinking about this a few years ago, when Bill James published an article in Slate on when a college basketball game is really over. Based on his observations, he was able to come up with an algorithm which predicts when a team has a safe lead, based on the lead, time left in the game, and who has the ball.

In baseball we can do the same kind of thing, except that there are only a finite number of logical stopping points (the end of a half-inning) and leads (the largest of which was less than 30 runs). Plus, we have a line score for just about every major league baseball game played since 1900, so we have a lot of data. This means that we don't need no stinkin' algorithm, we can give you the history probability that any given lead was overcome.

I didn't go all the way back to 1900. I stopped at 1948, because that was the data that was available using Retrosheet's Play-by-Play Files. With the Chadwick Software Tools we can go through all the games in the database and see how many times that, say, the visiting team was ahead by five runs after then end of the first, and how often the home won in that situation. Do that for all possible combinations of leads and innings and we get the table below. (You may have to widen your browser window to see everything.)

	Visitors Lead										Tie	Home Lead
Inn.	10+	9	8	7	6	5	4	3	2	1	0	1	2	3	4	5	6	7	8	9	10+
T 1	.077	.000	.040	.041	.083	.157	.187	.304	.378	.486	.591
B 1	.000	.000	.063	.056	.055	.132	.148	.236	.313	.416	.533	.643	.740	.819	.893	.912	.933	.960	1.00	.944	1.00
T 2	.000	.027	.067	.057	.080	.169	.180	.262	.345	.452	.580	.692	.781	.846	.925	.929	.969	.974	1.00	.941	1.00
B 2	.000	.016	.043	.032	.055	.134	.127	.218	.295	.397	.532	.656	.756	.829	.895	.920	.956	.966	.957	.976	1.00
T 3	.034	.032	.019	.043	.075	.133	.166	.250	.339	.456	.582	.705	.801	.863	.927	.939	.972	.984	.980	1.00	1.00
B 3	.015	.024	.016	.031	.047	.095	.122	.200	.278	.394	.526	.655	.766	.840	.909	.930	.958	.977	.986	.989	.990
T 4	.012	.021	.028	.033	.071	.096	.145	.225	.314	.446	.589	.719	.820	.886	.933	.963	.971	.988	.990	.994	.988
B 4	.004	.011	.022	.028	.039	.062	.094	.164	.252	.369	.526	.677	.788	.866	.917	.949	.969	.986	.990	.992	.996
T 5	.004	.009	.020	.031	.045	.078	.114	.199	.290	.422	.591	.741	.843	.904	.945	.967	.982	.995	.995	.991	1.00
B 5	.002	.005	.009	.019	.026	.049	.077	.139	.224	.344	.523	.692	.808	.887	.936	.962	.981	.989	.995	.993	.999
T 6	.001	.005	.011	.019	.031	.055	.091	.161	.268	.406	.600	.771	.873	.931	.964	.975	.994	.995	.999	.998	.998
B 6	.001	.000	.005	.009	.014	.030	.049	.097	.185	.305	.520	.725	.848	.917	.956	.972	.989	.994	.998	.999	.998
T 7	.001	.001	.004	.010	.016	.038	.061	.118	.217	.357	.610	.819	.912	.958	.977	.991	.995	.998	.999	1.00	.999
B 7	.000	.002	.002	.002	.007	.015	.033	.059	.130	.245	.523	.772	.894	.949	.973	.990	.995	.998	.999	.999	.999
T 8	.000	.001	.003	.004	.007	.017	.041	.074	.153	.296	.634	.890	.956	.985	.992	.997	.999	1.00	.999	1.00	1.00
B 8	.000	.000	.000	.001	.002	.006	.013	.027	.067	.150	.519	.865	.944	.981	.990	.997	.999	1.00	.999	1.00	1.00
T 9+	.000	.000	.000	.002	.003	.007	.014	.033	.071	.157	.615	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00
B 9+	.000	.000	.000	.000	.000	.000	.000	.000	.000	.000	.524	1.00	1.00	1.00	1.00

So what is all of this?

• The left hand column represents the situation at the end of either the top (visitor's) half of an inning, or the bottom (home team's) half, so T 7 describes the situation just before the Cubs let that day's designated karaoke singer ruin Take Me Out to the Ballgame. Since all innings after the ninth are played under the same conditions — the team ahead after the bottom of the inning wins, and if it's tied we try, try again — I combined all the data from those games into the labels T 9+ and B 9+.
• The other columns represent the lead by either the home team (on the right) or the visiting team (left) at the end of the half-inning. Ties are right in the middle.
• The decimal fraction in each block indicates the home team's chance of winning in that situation. So, for example, if you've just sat through an agonizing top of the third, when Yankees have scored a bunch of runs and lead by 7, we look at row T 3, column Visitors 7, and see that the Orioles have a 0.043 (4.3%) chance of winning the game — no, that's not true. Over the course of the last 60 years, 4.3% of all major league home teams down by 7 going in to the bottom of the third have come back to win. These are the Orioles, however, so they have about a 0.001% chance of coming back.
• I put all leads of ten or more runs in the 10 + categories. The software I wrote to write the table is easy to modify to list bigger leads, if you like.. I stopped at 10 because that will more or less fit on a standard blog page.
• The blank spaces are impossible situations. The home team can't score before it gets up to bat, so the right-hand side of the T 1 row can never be reached. And the home team doesn't need the bottom of the ninth or later inning unless it was tied or behind after the top of the inning. In that case they can never win the game by more than four runs.
• Indeed, I debated about putting in the B 9+ column, since it's a trivial case, but I wanted to highlight the fact that the home team still has a big advantage if the score is tied in late or extra innings. I've discussed this elsewhere.
• Finally, I haven't told you about the statistical significance of these results, i.e, the standard deviation or the sample size. Suffice it to say that there are more than enough events here for leads of nine runs or less. When we get to the 10+ category, especially in the early innings, there just aren't that many games. If you want to see the raw numbers (home wins/visitor wins/total games) drop me a line and I'll send them to you.
• Really last finally: I made a slight modification to the Chadwick source code to make it easier to parse the runs scored per inning. I also wrote a Perl script and some Fortran code to parse the output from Chadwick and write the HTML for the table above. If you're interested in the codes, drop me a line. If there's enough interest I'll make all the code available on my website.

Baseball After the All Star Break

Some years ago, I did a predictive study on how Major League Baseball teams would rank at the end of the season, based on their records at the All Star Break and the Pythagorean projection of future wins, based on the runs scored and allowed by each team.

It didn't work all that well. In particular, I predicted that Boston would win the AL East pennant, and Washington would be the NL Wild Card. That sorta didn't happen.

Nevertheless, I'll try again. Here's the table, based on the MLB standings at the All Star Break. The method is the same as last time, so you can read all about it there.

American League
East	W	L	PCT	Place	GB	RS	RA	Pyth	GL	PW	PL	TW	TL	PCT	GB
New York Yankees	53	35	0.602	2	1	455	334	0.637	74	47.14	26.86	100.14	61.86	0.618	0.00
Boston	55	35	0.611	1	0	482	371	0.617	72	44.42	27.58	99.42	62.58	0.614	0.73
Tampa Bay	49	41	0.544	3	6	380	343	0.546	72	39.35	32.65	88.35	73.65	0.545	11.80
Toronto	45	47	0.489	4	11	426	416	0.511	70	35.76	34.24	80.76	81.24	0.498	19.39
Baltimore	36	52	0.409	5	18	355	454	0.390	74	28.85	45.15	64.85	97.15	0.400	35.29
Central	W	L	PCT	Place	GB	RS	RA	Pyth	GL	PW	PL	TW	TL	PCT	GB
Cleveland	47	42	0.528	2	0.5	386	382	0.505	73	36.85	36.15	83.85	78.15	0.518	0.00
Detroit	49	43	0.533	1	0	413	421	0.491	70	34.39	35.61	83.39	78.61	0.515	0.46
Chicago White Sox	44	48	0.478	3	5	366	383	0.479	70	33.55	36.45	77.55	84.45	0.479	6.29
Minnesota	41	48	0.461	4	6.5	347	414	0.420	73	30.69	42.31	71.69	90.31	0.443	12.16
Kansas City	37	54	0.407	5	11.5	402	449	0.450	71	31.94	39.06	68.94	93.06	0.426	14.91
West	W	L	PCT	Place	GB	RS	RA	Pyth	GL	PW	PL	TW	TL	PCT	GB
Texas	51	41	0.554	1	0	457	404	0.556	70	38.91	31.09	89.91	72.09	0.555	0.00
Los Angeles Angels	50	42	0.543	2	1	355	330	0.533	70	37.32	32.68	87.32	74.68	0.539	2.59
Seattle	43	48	0.473	3	7.5	301	319	0.474	71	33.63	37.37	76.63	85.37	0.473	13.28
Oakland	39	53	0.424	4	12	315	339	0.467	70	32.66	37.34	71.66	90.34	0.442	18.24
National League
East	W	L	PCT	Place	GB	RS	RA	Pyth	GL	PW	PL	TW	TL	PCT	GB
Philadelphia	57	34	0.626	1	0	384	295	0.618	71	43.86	27.14	100.86	61.14	0.623	0.00
Atlanta	54	38	0.587	2	3.5	365	312	0.571	70	39.96	30.04	93.96	68.04	0.580	6.89
New York Mets	46	45	0.505	3	11	399	388	0.513	71	36.40	34.60	82.40	79.60	0.509	18.46
Washington	46	46	0.500	4	11.5	352	354	0.497	70	34.82	35.18	80.82	81.18	0.499	20.04
Florida	43	48	0.473	5	14	352	396	0.447	71	31.71	39.29	74.71	87.29	0.461	26.15
Central	W	L	PCT	Place	GB	RS	RA	Pyth	GL	PW	PL	TW	TL	PCT	GB
St. Louis	49	43	0.533	2	0	433	407	0.528	70	36.97	33.03	85.97	76.03	0.531	0.00
Milwaukee	49	43	0.533	1	0	405	406	0.499	70	34.92	35.08	83.92	78.08	0.518	2.05
Pittsburgh	47	43	0.522	3	1	354	346	0.510	72	36.75	35.25	83.75	78.25	0.517	2.22
Cincinnati	45	47	0.489	4	4	437	408	0.531	70	37.18	32.82	82.18	79.82	0.507	3.79
Chicago Cubs	37	55	0.402	5	12	375	459	0.409	70	28.63	41.37	65.63	96.37	0.405	20.34
Houston	30	62	0.326	6	19	358	464	0.384	70	26.89	43.11	56.89	105.11	0.351	29.08
West	W	L	PCT	Place	GB	RS	RA	Pyth	GL	PW	PL	TW	TL	PCT	GB
San Francisco	52	40	0.565	1	0	332	322	0.514	70	35.97	34.03	87.97	74.03	0.543	0.00
Arizona	49	43	0.533	2	3	416	407	0.510	70	35.70	34.30	84.70	77.30	0.523	3.28
Colorado	43	48	0.473	3	8.5	395	407	0.486	71	34.53	36.47	77.53	84.47	0.479	10.44
Los Angeles Dodgers	41	51	0.446	4	11	340	373	0.458	70	32.06	37.94	73.06	88.94	0.451	14.92
San Diego	40	52	0.435	5	12	304	338	0.452	70	31.63	38.37	71.63	90.37	0.442	16.34

Abbreviations:

• W: Current team wins
• L: Current team loses
• PCT: Winning rate
• Place: Current place in standings
• GB: Games Behind
• RS: Total Runs scored by team
• RA: Total Runs allowed by team
• Pyth: Pythagorean expected win rate. Following MLB, I used an exponent of 1.82 rather than the original James value of 2. It doesn't make a lot of difference, and didn't change the order.
• GL: Games left in season for the team
• PW: Projected wins in remainder of season, assuming they win at the Pythagorean rate
• PL: Projected Pythagorean loses
• TW: Total wins, current + projected Pythagorean
• TL: Total loses
• PCT: Projected final winning ratio
• GB: Projected final games behind

OK, not a lot of changes going on. Despite an anemic offense, San Francisco's fantastic pitching will keep them in first in the NL West. Philadelphia will win the NL East going away, even though Atlanta wins the NL Wild Card. Texas will hang on in the AL West.

There are a few predicted swaps, highlighted in yellow: St. Louis will pull ahead of Milwaukee. And Cleveland will (yawn) edge out Detroit. Surprisingly, the only changes occur at the top, which probably says something about competitive balance in MLB.

And, finally, Red Sox will be the AL Wild Card. Which means …

Frak

The Home Team Wins Most Ties (In Baseball, Anyway)

I've been playing around with Retrosheet to see how often a baseball team wins a game if it's, say, five runs ahead at the end of the fourth inning. I plan to get that up sometime during this long weekend, but while doing the study I found another interesting result.

Retrosheet's Play-by-Play files, along with the cwevent program from Chadwick, let you extract all sorts of information from almost every MLB game played between 1950-2010. From that data I extracted every game that was tied at the end of a half-inning, and figured out who eventually won. Then I counted up the number of times the home team won for each half-inning. The results are shown below:

Click on graph to see a larger figure

The black diamond represents the situation at the start of the game, the red diamonds the situation where the game is tied in the middle of the inning, and the blue diamonds when it's tied at the end of an inning. We'll get to the error bars in a minute.

So what is all of this? Well, at the beginning of a game the score is obviously tied, so that should be part of the study. So if we look at all 115,748 games in the database, we find:

• The Home Team won 62,418 games,
• the Visiting Team won 53,192 games, and
• there were 138 games that were tied when the game was called.

If we throw out the ties, then the Home Team won 53.990% of the games that went to a decision. That's the black diamond at the far left of the graph. The 54% win rate is baseball's version of Home Field Advantage, and it has been very constant:

Click on graph to see a larger figure

I performed the same calculation for all the games which were tied at the end of a half inning. For example, if the game is tied at the end of the fifth, the home team has a 52.0% chance of eventually winning the game. Tied after the top of the sixth? It's up to 60%.

So what do the error bars represent? Basically they give you an idea of the number of games in the sample. Suppose that in a given game the home team wins with probability p. Then in an N game sample the probability that the home team wins n games follows the binomial distribution, e.g.

             N!        n      N-n
P(N,n) = -----------  p  (1-p)
          n! (N-n)!

If we look at a large number of N-game samples, then we'll find that on average the home team will win N p games, which makes sense. The standard deviation will be [N p (1-p)]^½. Since the graph normalized everything by the number of games played, the error bars are the standard deviation divided by N, or [p (1-p)/N]^½. Since most values of p are between 0.5 and 0.7, wider error bars basically tell you that fewer games have gotten to that point. And when the error bars get really wide, as they do after the fourteenth inning or so, it says there aren't enough statistics available to give you meaningful information.

What does it all mean, you ask? Well, first it says that the home team advantage is real. Why there is a home field advantage is another question, and there is not enough information here to answer that question.

Then there's the observation that the home team has a larger advantage if the game is tied in the middle of the inning than it does if the game is tied at the end of an inning. That's just common sense. In the middle of the fourth inning, the visiting team has five more innings at the plate. The home team has six — five for sure, and one more if they need it. This isn't the home team bats last advantage, it's the home team gets one more at-bat than the visitors advantage, not the same thing.

Next, we see that if the game is tied at the end of an inning, the home team's advantage decreases slightly, so that at the end of eight innings it's only 51.94%. Presumably that's because the home team does have some advantage in being at home, but as the game progresses they have less and less chance to use that advantage. After the fifth inning the home team's advantage oscillates around 52%, down from the 54% advantage they had at the start of the game.

Indeed, the fact that the blue dots go down from innings 1-4 suggests that the home team bats last advantage isn't worth a whole lot. If it was, you'd expect the advantage to be greater in tie-game situations as the game wears on, because that last at-bat becomes a larger and larger proportion of what's left of the game.

Finally, there is that dip in the red diamonds between the first and second inning. If the game is tied going into the bottom of the first, meaning that the visiting team didn't score, then the home team will win 59.15% of the time, with a standard deviation of 0.17%. If the game is tied going into the bottom of the second, however, then the home team only has a 58.01% chance of winning, with σ = 0.22%. That's a five-σ change in the probability, which I would think is statistically significant. The win rate is pretty much constant in the third inning ( 58.16% ± 0.27%) and then starts going up, as you'd expect, since the home team has proportionately more at-bats than the visitors at the middle of an inning.

Why is this so? I have no idea. All I can say is that if your a visiting baseball team, it's better to be tied with the home team in the middle of the second inning than it is to be tied before the home team comes to bat. And you better be ahead by the middle of the third.

The information used here was obtained free of charge from and is copyrighted by Retrosheet. Interested parties may contact Retrosheet at www.retrosheet.org.

Why I Hate March

I just spent the last three hours not watching the Kansas-Boston University first round NCAA game. In case you didn't join me in not watching, KU spent the first 25 or so minutes wondering why the BU players weren't genuflecting. Then they concentrated for a few minutes, and the game was over. Because of course, you know, when #1 meets #16, #16 has never won, right?

Trust me. Some day it will happen. And which team will that be? We'll, let's look at the past for some clues, starting here:

• 2010: #1 Seed, lost in second round
• 2007: #1 Seed, lost in Sweet 16
• 1998: #1 Seed, lost in second round
• 1997: #1 Seed, lost in Sweet 16
• 1995: #1 Seed, lost in Sweet 16
• 1992: #1 Seed, lost in second round

Not to mention Bradley, or Bucknell, or the suffering I did during the Ted Owens years.

It's enough to make one start a website, except a) someone beat me too it, and b) Roy was as bad, or worse, because he didn't win a Championship until he clicked his heels three times and went home.

But mark my words, the #1 Seed in NCAA history to lose in the first round will have a six-letter name on the front of the jersey, and have two Crimson and Blue mascots that look something like this.

Rubbing It In

Before every game in the Major Leagues, workers unwrap 100-200 brand-new, shiny baseballs, each bearing Bud Selig's signature.

You won't believe what they do to them.

Well, It's Over

and there are no Kansas teams left playing basketball this year.

Some thoughts:

• K-State's loss to Butler was, in large part, due to the fatigue left over from the Xavier game. Just this morning, the Washington Post ran a story of the great KSU conditioning program.
• At least the left side of everyone's bracket is blown up as badly like mine. I doubt there were more than 100 people, all from Indianapolis, who had Butler in the Final Four.
• And, as will be noted 5,000,000 times between now and next Saturday, the Butler gym was the scene of Milan's historic defeat of Muncie Central in 1954.

Damn

Well, at least K-State is still in it.

You have no idea how painful that is to write.

One Down

Phew!.

All year, every game, KU has always played down to to the opposition. And it's going to come back to bite them before we're done, certain prognosticators notwithstanding.

It will be a morality play, just like Memphis can't hit free throws turned out to be in 2008.

My bracket, and my heart, say KU wins it all (it's sort of mandatory for KU alums), but if I had to bet, my wallet would say it ends next week.

And I'd never be happier to lose a bet.

Who Woulda Thunk?

Bobby Knight and I go way back — I was at Indiana University from 1973-79, including the great undefeated 1975-6 team featuring Scott May and Quinn Buckner. Over the years I've had the chance to notice that when he's coaching he's not particularly stable.

However, over the last year Knight and Brent Musburger telecast Big XII basketball games, mostly those involving KU and/or K-State. I've also heard him on the Mike & Mike radio show on my drive to work.

When he's not coaching, Bobby Knight is smart, witty, thoughtful, and doesn't just talk to fill the air. It's amazing.

Of course, I have no way of knowing what he's like off-camera, but it's a considerable improvement over the old Knight.

Maybe he should have started out as an announcer.

Doubly Bad Seasons

At the end of this season, the Washington Nationals finished as the worst team in baseball, with a record of 59-103 (0.364). To add insult to injury, the Baltimore Orioles finished as the worst team in the American League, 64-98 (0.395). So if you had the misfortune to watch all the Orioles and Nationals games on MASN, you saw a combined record of 123-201 (0.380).

This set me to wondering how bad this really is. In most two-team markets, when one team is up, the other one is down, right? Well, not always. I went looking through the season standings in Retrosheet, searching for two teams in the same market that finished at the bottom of the division.

Read more ⇒

A Pretty Good Average

June 12, 2011: This post completely fraks up the calculation of David Smyth's Base Runs statistic. I've now fixed that, and added the data from 2009 and 2010. You can find all the updated tables here.

I'm a big fan of Sabermetrics, the use of statistical information to understand how baseball teams win games. Part of this is my love for the game, part my natural tilt toward numerical data, and part is that I've always enjoyed reading Bill James's work (full disclosure: he and I overlapped at KU, though we never met). Not to mention the fact that, from my desk, I can see several editions of both The Baseball Encyclopedia and Total Baseball.

But … in the old days, we judged batters by average (> 0.300 is good), home runs (> 30), and runs batted in (> 100). That was it. These stats have some problems: batting average doesn't tell you how many times a guy gets on base by walking, you can only bat in runs when your teammates are already on base, and as for home runs — well, OK, home runs are a pretty fair way to determine part of a players value.

The inadequacy of the traditional trio of AVG/HR/RBI led to the development of new measures for player performance: On-base percentage, slugging average, runs created, etc., etc. The problem is that off the top of my head I don't know what's a good number for any of these statistics. OK, a slugging percentage of 0.900 is better than 0.400, but is a player who slugs 0.500 a power hitter, or just Joe Shlabotnik?

Read more ⇒

The Back of the Ticket

From the back of the Washington Nationals Baseball ticket for May 15, 2009. Footnotes added.

By the use of this ticket, the ticket holder agrees that: (a) he or she shall not transmit or aid in transmitting any information about the game or related activities to which it grants admission,^a including, but not limited to,^b any account,^c description, picture,^d video,^e audio,^f reproduction^g or other information concerning the game or related activities^h (the Game Information); (b) the Club issuing the ticket is the exclusive owner of all copyrights and other proprietary rights in the game, related activities,ⁱ and Game Information; and (c) the participating Clubs, Major League Baseball Properties, Inc., Major League Baseball Enterprises, Inc., MLB Advanced Media, L.P. and each of their respective affiliates, licensees and agents shall have the perpetual^j and unrestricted right and license to use his or her name,^k image, likeness^l and/or voice^m in any broadcast, telecast, photograph, video and/or sound recordingⁿ taken in connection with the game for all purposes and in all media^o known and unknown^p throughout the universe.^q Breach of any of the above will automatically terminate this license and may result in further legal action.^r

^aSo if you sneak into the game, you can do any of these things?

^bDon't worry, we'll think of other things later

^cWhich is why I can't actually tell you about the game

^dNo snapping pictures with your cell phone

^eRemember Sonny ripping the film from the camera? Applies here, too.

^fThe cell phone conversation, where you called your wife to tell her the game was going into extra innings? Verboten

^gEven with sock puppets

^hSuch as the young woman bouncing up and down three rows in front of you

ⁱThis includes all bubble gum, sunflower seed shells, and tobacco wads spit out by players during the game

^jOne of the five people you meet in heaven will be an MLB Lawyer. Oh wait, if there's a lawyer there …

^krcjhawk will now forever be associated with the Washington National Baseball Club

^lWell, I don't suppose they'll be using my likeness, but that woman three rows down …

^mI have been told that I have a voice made for blogging.

ⁿWait! We left out Leroy Neiman pantings!!!

^oWhew. For a moment I'd thought we'd left a loophole.

^pJust in case sending pictures via DNA encoding ever becomes popular

^qAt least on Arcturus they don't complain about us calling our championship the World Series

^rA century from now, if we find you put a picture of this game in a scrapbook, we'll exhume your body, drag it to the site of the Spanking-Brand-New Washington Nationals of Boise Park, and cast it out through the front gate. Then we'll sue your heirs.

Download Today's Sports

For years, The Sporting News was the bible of the Baseball world: complete records, weekly stats, team information, publishing yearly guides, rule books, etc. As a bonus, it would cover other sports as well, at least during baseball's off season.

Many of TSN's services have been replaced by such things as Retrosheet, the Emerald Guide, and, of course, the web sites of ESPN, Sports Illustrated, and even TSN's own site.

Trying to remain relevant while its popularity steadily declines, TSN has come out with Sporting News Today. If you are looking for investigative journalism, look elsewhere. SNT is a daily collection of articles, mostly from wire services, about what happened last night in sports. The four biggies (baseball, basketball, football and hockey), mostly, but other stories show up as well. At 40+ pages per day, it's the sports section your local paper never had, even when you had a local paper, and it had a sports section. For those of us far way from home, it's a godsend.

You can read SNT on the web, but I've found the best viewing is with the PDF download. This is predictably named, so I wrote a short script to pull it off the web, open it up with my favorite PDF viewer, and erase itself when I'm done. Feel free to modify this as and where you will. (With a few obvious modifications, it's excellent for looking at many daily comic strips.)

#! /usr/bin/perl

# Should download PDF version of Sporting News Today
# You probably should register first

# Make sure everything goes on in /tmp:

chdir "/tmp" ;

# Find the date

$daystring = `date +"%m %d %Y"`;

chomp($daystring);

@date=split(" ",$daystring);

$m = $date[0];
$d = $date[1];
$y = $date[2];

$name="snt".$y.$m.$d."-dl.pdf";

$address="http://today.sportingnews.com/sportingnewstoday/".$y.$m.$d."/data/".$name;

# Get it:

system("wget $address");

# Read it, then erase it when you're done

system("xpdf /tmp/$name; rm /tmp/$name");

Just in Case You Are Still Naive Enough to Believe Your Secrets are Safe

Rodriguez, A., On the propensity for unfavorable information to leak even though they tell you that it will absolutely, positively, be destroyed, ESPN (2009).

Something You'll Never See Again

A little more fun with the Major College Major Sports in 2007-8: I found 239 teams which played both Division I Men's Basketball and either Division I (Bowl Subdivision) or Division I-A (Championship Division) football. The table below presents the wins, losses, and winning “percentage” for Football and Basketball. I then averaged the two percentages, to compute a “Combined Percentage,” and ranked the schools in order according to that. Fans of Kansas football (remember the slogan: “A Tradition Since September”) will note that this year was, indeed, special.

This table just shows the top 25. I've put the complete list elsewhere. Let me know which teams I've missed, there are probably several.

Rk	School	Football			Basketball			Comb.
		W	L	Pct.	W	L	Pct.	Pct.
1	Kansas	12	1	0.9231	30	3	0.9091	0.9161
2	Brigham Young	11	2	0.8462	27	7	0.7941	0.8201
3	Dayton	11	1	0.9167	21	10	0.6774	0.7970
4	Tennessee	10	4	0.7143	28	4	0.8750	0.7946
5	Texas	10	3	0.7692	27	6	0.8182	0.7937
6	Wisconsin	9	4	0.6923	29	4	0.8788	0.7855
7	West Virginia	11	2	0.8462	23	10	0.6970	0.7716
8	Boise State	10	3	0.7692	24	8	0.7500	0.7596
9	Memphis	7	6	0.5385	33	1	0.9706	0.7545
10	Southern California	11	2	0.8462	21	11	0.6562	0.7512
11	Northern Iowa	12	1	0.9231	17	14	0.5484	0.7357
12	Oklahoma	11	3	0.7857	22	11	0.6667	0.7262
13	Massachusetts	10	3	0.7692	21	10	0.6774	0.7233
14	New Mexico	9	4	0.6923	24	8	0.7500	0.7212
15	Connecticut	9	4	0.6923	24	8	0.7500	0.7212
16	Ohio State	11	2	0.8462	19	13	0.5938	0.7200
17	Appalachian State	13	2	0.8667	17	13	0.5667	0.7167
18	North Dakota State	10	1	0.9091	14	13	0.5185	0.7138
19	San Diego	9	2	0.8182	20	13	0.6061	0.7121
20	Drake	6	5	0.5455	26	4	0.8667	0.7061
21	Clemson	9	4	0.6923	23	9	0.7188	0.7055
22	Davidson	6	4	0.6000	25	6	0.8065	0.7032
23	Southern Illinois	12	2	0.8571	17	14	0.5484	0.7028
24	Western Kentucky	7	5	0.5833	25	6	0.8065	0.6949
25	Arizona State	10	3	0.7692	19	12	0.6129	0.6911

Baskeball Rule Changes I'd Like to See

While watching the (fantastic) NCAA Men's Basketball tournament, I realized that there are a few rules I'd like to see changed. One I've mentioned before, but the others are new, at least for me.

• As I mentioned before, eliminate the ability to call time out when you're about to loose the ball. In the Monday night game, during overtime, I think, there was a play where a half-dozen players were on the floor going after the ball. Someone tried to cradle the ball and call time. And we've all seen people call time as they're flying out of bounds. Why do we allow this? If you're trying to put the ball in play from out of bounds, you can't call time after the four second count. You can't call time after you've spent eight seconds in the back court. Yet you can call time as you catch an errant pass while you're flying out of bounds, if you can get your hands into a “T” before you touch down.
  
  In the timed cases mentioned above, the purpose of the rule is to forbid a team to call time out before a change of possession. Well, in the case in Monday's game, or the guy-flying-out, a change of possession is about to occur. So why can you call time?
• Move the three-point line out another foot or so. Looks like this will happen in the fall.
• Except in the case of injuries, a substitute has to stay in the game for two changes of possession: this eliminates the offense-defense substitutions you see at every whistle during the end game, which tends to slow things down immensely. I noticed this while watching taped games during the tournament. My DVR has a ten-second advance button. When I heard the substitution buzzer sound, I'd hit the advance once. I never missed a single second of play, or a free throw.
• And here's the biggie: Eliminate the Hack-a-Shaq. Kansas won the game because they did something illegal: they fouled. Repeatedly. That Memphis didn't take advantage (and I'm glad they didn't) is the Tigers' fault, but the point is, KU took advantage of a bad rule. It gets worse, at least from my point of view: in the last ten seconds of regulation, Memphis was trying to foul someone, anyone, so that Kansas would get two free throws, rather than a chance to hit a three and tie the game. A Memphis foul, an illegal act, would most likely win them the game. OK, KU could have done the make-the-first, miss-the-second, trick, but then they'd have to get the rebound and put it back up with only five or six seconds left, at best.
  
  Not to mention, the endless trek to the foul line at the end of a close game is boring. It doesn't help your ratings, CBS, got that?
  
  So let's change the rule: let the team fouled in the last two minutes have its choice, assuming they're in the bonus:
  • On One&One fouls, if the team makes the first shot, they can either shoot the second shot or take the ball out of bounds.
  • On Two shot fouls, the team can take the both shots, or take one shot and then get the ball out of bounds.
  • On Three shot fouls, it's two shots plus ball.
  • Of course, on flagrant fouls, it's still two shots plus the ball.
  • You still only get one shot if you're fouled in the act of shooting and make the basket.
  • If you're not in the bonus yet, you get the ball out of bounds, same as now.
  This change would speed up the end-game considerably, as well as keeping a team from profiting while fouling. (Sorry, Memphis, it's not retroactive.)

Anybody else like any of these?

Note added in proof: I swear I did not read this until after I first posted the above.

The Game

KU 75, Memphis 68 (OT)

The First Night

Of course, I got tickets to the official opening game at Nationals Park, watching the Braves play the Nationals on Sunday.

Those of you outside the DC area might not know that they built the park with very little parking. As a 20-game ticket holder, I could have gotten a parking pass for $20-$35 per game, but it's a difficult part of town to get in and out of, so I decided to use one of the other two options: Metro, or the National's unique shuttle service, where you park (for free) in a lot at old RFK Stadium, and then take a shuttle bus to the new park, all for free. Must cost the Lerners a bundle. Tonight, I decided to take the shuttle, which winds out of RFK, onto the Southeast-Southwest Freeway (I295/395), past the Marine Barracks, and dumps you about three blocks from the stadium. All in all it went pretty smoothly, but this was a Sunday night. How things will work during rush hour is anyone's guess.

Since I had tickets up (way up) above first base, when I got to the park

I headed for the first base entrance, where I found a rather long line:

The problem, of course, was that Dubya was present, so we all had to go through metal detectors. That's fine, except the first base side only had four. After an hour or so, someone from the Nationals finally got a clue, and told us that in right center field there were twenty (count 'em, 20) gates, and small lines. Gee, thanks guys. There were dozens of Nats employees hanging around, saying “gee, look at the long lines” for an hour or more, and they finally say something at about 7:50, for an 8pm start.

I eventually got in, and up the the main concourse in time for the National Anthem

and, thanks to the wonders of TV commercials, up to my seat (second row from the top, though our regular seats will be much closer to the field) in time for the first pitch

Odalis Perez to Kelly Johnson, for a strike.

The Nats scored twice in the first inning, then made 24 straight outs before Ryan Zimmerman ended it with a walk-off homer with two out in the bottom of the ninth. During the game, I took a bit of a walk-about, and got this picture of the Anacostia waterfront, which looks a lot better at night than it does in the day.

All in all, the park looks to be a pretty good place to see a ball game. The main question is whether or not 40,000 people can get to it during a DC rush hour, or out of it after a day game. But as a place to watch baseball, it looks like it's going to be a winner.

(Photo from Sports Illustrated, motivational design from Motivator.)

I didn't watch all of the Davidson-KU game yesterday, because I was waiting in line for the Washington Nationals' Season/Home/Stadium opener – more about that in another post.

I did watch most of the first half, except for the ten or so minutes after the first TV timeout when I turned the TV off because my blood pressure was getting up to pre-medication levels.

KU was cold, nervous, and generally not playing up to its potential – if you have to rely on Sasha Kahn to save you, then you're in trouble. Fortunately they managed to “contain” Stephen Curry, holding him to 25 points – almost half of Davidson's total.

So next week we get to play Roy – the first time that Carolina and Kansas have played since the divorce. I don't know what to think. Did KU get its bad game out of its system, or is it really, truly, the fourth-best team in the Nation, just like they were seeded? At least if they are officially the underdog they will probably be a lot looser than they were on Sunday.

And, way back in about 1990, a Kansas coach who had never been to the Final Four led his team to a victory over Dean Smith in the semifinals. I think it's about full-circle time.

Kansas by five.