<http://schema.org/articleBody>
"&lt;p&gt;&lt;/p&gt;&lt;p&gt; Should scores be more accounted for in evaluating matchups between underdogs and more heralded teams? (Chuck Liddy/Raleigh News &amp;amp; Observer/MCT via Getty Images)&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Jeff Sagarin is one of the Godfathers of data-based sports projections, having started earning money for his work in the early 1970s after graduating from M.I.T. The New Rochelle, N.Y., native has provided rankings to\u00A0&lt;em&gt;USA Today&lt;/em&gt;\u00A0since the mid-1980s and also has provided advisory services to multiple professional sports franchises. His Sagarin Ratings were one of the rankings used in college football's BCS calculations and the NCAA tournament committee also has access to his work.&lt;/p&gt;&lt;p&gt;SI.com was able to catch up with Sagarin last Friday for a captivating conversation. Part 1 of the discussion, in which Sagarin provided depth and color to how he got started in his profession, is here. Today in Part 2, Sagarin talks more about current-day applications and the understanding of projections in sports.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;/p&gt;&lt;p&gt;SI:&lt;/strong&gt; I\u2019ve spoken to (current NCAA tournament overseer) David Worlock and said repeatedly \u201CWhat is the issue with having some margin of victory cap or diminishing returns in the formula, so there\u2019s a sportsmanship aspect?\u201D You can\u2019t sit here and look at one team beating someone by two points and say [that\u2019s] as good as another team that beat them by 21. It\u2019s not true.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Jeff Sagarin:&lt;/strong&gt; You can show in the RPI where a bad team is going to play a good team -- they\u2019d have to be pretty far apart in rating -- and then the crappy team, by the act of playing the game, will gain rating even if they lose, and the better team, will lose in the rating, even if they win the game.&lt;/p&gt;&lt;p&gt;People would say \u2018Well, gee, Jeff, that could happen in your system.\u201D Yeah, but I\u2019m using scores. So hypothetically, let\u2019s say Duke were to beat Grambling by one point at Cameron Indoor, yeah, Duke should go down in rating and Grambling should go up. But in a system that\u2019s measuring metric is winning and losing, that doesn\u2019t even know the score, it\u2019s wrong that Duke should go down and Grambling should go up by the virtue of them playing and Duke winning. Because, remember, it doesn\u2019t know the score, and even if it did, it can\u2019t use it.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;SI:&lt;/strong&gt; Do you care about game results as they apply to your ratings, even though we understand they\u2019re one-game samples?&lt;/p&gt;&lt;p&gt;&lt;strong&gt;JS:&lt;/strong&gt; Let\u2019s say with the NCAA championship game in basketball, it was Louisville and Michigan. I had Louisville to win by four or whatever the betting line was, but let\u2019s say hypothetically I had Louisville to win by 27 and they won a real close game, yeah, that would make me feel \u201CDarn, I wish they had won by closer to 27.\u201D So, yeah, basically I find myself rather than having a personal attachment to something, I find as my friends say, \u201CWhat a surprise! Jeff Sagarin is rooting for himself.\u201D&lt;/p&gt;&lt;p&gt;&lt;strong&gt;SI:&lt;/strong&gt; What did you personally learn working for professional teams? I know you were working for the Dallas Mavericks for awhile. And what did you take away as far as where pro teams are with data acceptance?&lt;/p&gt;&lt;p&gt;&lt;strong&gt;JS:&lt;/strong&gt; It\u2019s always interesting. The last season we were with Dallas, I believe, was the 2010-11 season I guess when they won everything. We have not been with them the last two seasons. Me and my friend Wayne Winston, we\u2019ve been with the New York Knicks.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;SI:&lt;/strong&gt; Well, that explains everything. I\u2019m a Knicks fan.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;JS:&lt;/strong&gt; Well, the Knicks got better this past year. We helped them!&lt;/p&gt;&lt;p&gt;&lt;strong&gt;SI:&lt;/strong&gt; Without prying into proprietary information, do you ever read coverage of a team you\u2019re advising? People saying \u201CWell, they can\u2019t play this way or Carmelo Anthony can\u2019t go so at much as he does?\u201D&lt;/p&gt;&lt;p&gt;&lt;strong&gt;JS:&lt;/strong&gt; I\u2019ll give you something even better than that. One of my friends from my home neighborhood in New Rochelle, he\u2019s a huge Knicks fans, so I\u2019ll tentatively tell him some of the secret stuff, we got so-and-so, and he says, \u201CThat\u2019s bull---! This is totally wrong!\u201D&lt;/p&gt;&lt;p&gt;&lt;strong&gt;SI:&lt;/strong&gt; I say to my readers all the time: If I could pick games with 60 percent accuracy, I\u2019d be living in a big mansion in Nevada.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;JS:&lt;/strong&gt; People don\u2019t understand. When I first started and people started reading what I was doing in the fall of 1972, I was only 24, and I was so cocky, I said \u201CI\u2019ll put those guys in Vegas on their backs. I\u2019ll show them who\u2019s the boss.\u201D Well, I found out that guys with the diamond pinky rings put out pretty good odds. Damn good odds. When I started challenging them week to week, sort of using a pure math, thinking-it-through strategy, the cliche I arrived at was \u201CReal life is under no obligation to perfectly conform to a mathematical model created by a human being.\u201D&lt;/p&gt;&lt;p&gt;&lt;strong&gt;SI:&lt;/strong&gt; That\u2019s what I was getting at. People believe if you\u2019re a quant guy, you only believe in numbers, and I see this trend and this team should win by six. And I say, I start with stats but there\u2019s no substitute at the end of the day for watching games or watching tape to either support what your initial belief is or counter it.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;JS:&lt;/strong&gt; You end up recalibrating your model to reflect what\u2019s actually happening. It\u2019s the model\u2019s obligation to reflect real life. Not real life\u2019s obligation to reflect your model.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;SI:&lt;/strong&gt; I guess that goes back to when you go to tweak your models. You said you\u2019re comfortable where you are with your models\u2026&lt;/p&gt;&lt;p&gt;&lt;strong&gt;JS:&lt;/strong&gt; Oh, I tweak them. I have the results of all the games for any given time segment and I\u2019ll test to see what would have worked the best. Then my goal is to find what would have worked the best for this season, what would the parameter be for last season. I like to see parameters that are similar, for an optimal parameter that works from year to year. If one year, the parameter is 1.1 and then the next year, it\u2019s 99.7 and then the next, it\u2019s 61.3, that doesn\u2019t give you much confidence.&lt;/p&gt;&lt;p&gt;Luckily, the way I have been analyzing lately, I have been getting similar parameters, so I can live with this. You\u2019ve put in as much effort as you can, and at a certain point, you can\u2019t get any more blood out of a stone. If you\u2019re trying to look at average points error in these games, even if the season is totally over and you\u2019re just retro-fitting it, there\u2019s a limit as to how accurate you can be. Could I go back retroactively and get the right score in every game? No.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;SI:&lt;/strong&gt; I think it\u2019s a philosophical thing. You can have a ratings system that\u2019s as good as anything, but at the end of the day can\u2019t account for human interaction or account for where you have a bunch of 80-20 situations that sometimes break the 20 percent way.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;JS:&lt;/strong&gt; Here's an example. I was rooting for San Antonio to beat the Miami Heat because those guys never shoot their mouths off, they come to work and don\u2019t have their fingers pointing at their own shoulders, like look at me! To have them lose with 25 seconds left, up by five, and they\u2019re going to be at the free throw line two more times [San Antonio had two free throws on their next possession]. If you simulate that situation, up five, other team has the ball, but you\u2019re going to be at the free throw line two more independent times, it\u2019s almost impossible to lose.&lt;/p&gt;&lt;p&gt;They\u2019re going to have two threes they\u2019re going to have to make, you\u2019re going to have free throws you have to miss. It was incredible that they lost that game. Miami had to get a rebound on the three that tied the game because they actually missed the first three and got the rebound as time was expiring on the second three.&lt;/p&gt;&lt;p&gt;Yet, there\u2019s going to be some pompous talking head say, \u201CObviously, San Antonio didn\u2019t have the killer instinct. Miami showed the heart of a champion.\u201D They got lucky, and San Antonio was the victim of random fate.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;SI:&lt;/strong&gt; Sports are not like chess, which is one on one with a finite number of defined moves, but I would argue that any sport is to some extent solvable. As a mathematician, how close do you think we can get to \u201Cperfect play\u201D in any kind of sporting event?&lt;/p&gt;&lt;p&gt;&lt;strong&gt;JS:\u00A0&lt;/strong&gt;I think it\u2019s laughable. It\u2019s impossible. Put it this way. Let\u2019s say you have a ping pong table in your rec room and you have two of your kids play each other, or you\u2019re one of the people playing and you play your next door neighbor every night, two out of three because you like to and it keeps your reflexes going. Is the score of every game the same? No. Some nights, he might skunk you, beat you 21-7. The following night, you might beat him 21-14. You\u2019re still the same human beings. What happened? There\u2019s just a randomness to life. That\u2019s called being on a random curve.&lt;/p&gt;&lt;p&gt;Everyone talks about the bell curve. There\u2019s a million curves in math, all similar to the bell curve. There\u2019s nothing magic about the bell curve, OK? But the point is that whatever the distribution around the mean is, there\u2019s always going to be a randomness. After the season is over, I\u2019ll tell the program \u201COK, now with the ratings you have, plug them back in,\u201D [and you see] how many you got right and how many you have wrong and what\u2019s the margin of error.\nThe average margin of error after the season is over, plugging it back in -- it depends on the sport, what the margin is -- but let\u2019s say 10 points a game error. It\u2019s randomness. You can\u2019t penetrate that. The National Security Agency could not come up with an algorithm [better than that]. You can\u2019t get lower than a certain number based on the specific scores. A lot of times they call that the \u201Cleast squares fit.\u201D You can\u2019t do better than that. And that number is always, in basketball or football, 10, 11, 12, 13 points. And that\u2019s just the way it is.&lt;/p&gt;"^^<http://www.w3.org/2001/XMLSchema#string>
<http://www.si.com/college-basketball/one-and-one/2013/08/27/verbatim-stats-guru-jeff-sagarin>