Monday, January 23 at 6:15 PM ET
It is very important to us to be transparent and honest about our picks. I always try to recap each football weekend. With the addition of the TrendFinder database, which tracks all performance for all of our information (against published lines in every sport), the blog will focus more on topics beyond performance. Our performance is now as transparent as it can possibly get. We will still do our best to note areas of strength, while acknowledging areas of weakness - honing in on what this means to subscribers as it applies and touching on other, "big picture" topics in the process.

This blog will review the NFL Conference Championship results, the content schedule for the Super Bowl, basketball engine improvements, basketball power rankings, college basketball homecourt advantages, future blogs and college ATS wins by team. Next week, we will discuss the live NFL application that will launch for the Super Bowl (including an example chart from the AFC Championship Game) and have a formal announcement on hockey (testing is illustrating decent O/U value). Check out this Wall Street Journal article for a sneak peek at some of the information that can come from our live NFL analysis.

As a reminder, at midnight ET each day, we make all of our previous day's subscriber content available for free for registered users. Performance is also tracked in the TrendFinder Database (updated every morning from the previous day). We're never going to hide anything. So even though we have to swap out articles in the archive to focus on new ones, articles never go away. Just make sure to use the correct week and date in the URL - or ask us for the link...

NFL Conference Championship Weekend:
Conference Championship weekend featured two great games that played out relatively similar to our expectations. We had predicted that New England would defeat Baltimore (+7.5) 27-21 (U 50.5) and that the Giants (+2.5) would win outright at San Francisco 24-22 (O 43). In the AFC Championship game, our predicted halftime score was the same exact projected score, 27-21, as our pregame prediction. Ultimately, New England won, 23-20, after Billy Cundiff missed a field goal with 11 seconds remaining that could have tied the game. In the NFC Championship game, defenses played a little better than expected (and I still think conditions were a little overblown by the media coverage of the game, but did end up being a little worse and playing a little more of a role than we had anticipated), but the Giants were still able to pull off the upset, 20-17, in overtime. Those outcomes gave us a 3-1 ATS and O/U weekend, with the only loss coming from the OVER (43) in the NFC Championship game. For this postseason, ATS picks are 7-3 (70%), and all playable picks are 12-6 (67% ATS and O/U). Since we launched PredictionMachine.com the week before Super Bowl XLIV, our NFL Playoff picks are 19-3 ATS (86%). The formula that includes full season's worth of player and team data, easy access to injury information and the influx of wagers from the exploitable and biased public (the side in each game this season that has been wagered on more is just 5-4-1 ATS, with essentially 50/50 money coming in on NYG @ GB) has historically led to the best performances by this type of technology (as is also the case during the NCAA tournament).

We will talk more about the key points to the games, most notably with the AFC Championship, when reviewing the live projection chart next week, but there were a few observations that I wanted to mention. First, in this postseason, we have been on the side of the team with the higher regular season sack rate (defensive sacks/pass plays faced) in every game. That is not necessarily a fluke. The public sees the offensive numbers and overvalues teams with elite quarterbacks. We see the value of elite pass rushes and their abilities to neutralize elite quarterbacks. This formula likely will not work forever, but it makes total sense now... That being said, both teams still remaining in the playoffs have elite quarterbacks.

Another formula helping teams win that cannot work forever that was on full display this weekend involved game winning or tying field goals in the waning seconds. Research suggests that the impact of icing a kicker in the NFL is very minimal. However, at this point, there actually appears to be some value in not icing a kicker. In the NFC Championship game, the 49ers still had two timeouts and the Giants had three when New York lined up for what could (would eventually) be the game-winning field goal in overtime. The Giants' kicking team took its time, assuming San Francisco would use a timeout to ice the try. When the 49ers elected not to ice the kicker, New York just sat there confused until the team picked up a delay of game penalty (I am someone who typically yells at the TV when teams waste timeouts to avoid delay of game penalties - five yards is not usually worth a timeout - but this was egregious). Lawrence Tynes (I like that guy today; I did not like him 24 hours ago) had to make a field goal from five yards further away almost exclusively because San Francisco did not ice the kicker. Then, in the AFC Championship game, Baltimore still had a timeout remaining (because it had foolishly saved the timeout instead of run the ball on third and one on the previous play to give its team up to three more shots at the endzone) when the Ravens lined up for what could (would not eventually) be the game-tying field goal with eleven seconds to go. As the play clock ticked down and New England, which had three timeouts, elected not to use any of them to ice the kicker, Baltimore rushed the snap and kick. With 50 seconds left in the game, down three points and driving deep into New England territory, Baltimore was 68.2% likely to win the game. They cost themselves all of that chance by mismanaging the next 30 seconds. Kudos to Bill Belichick and Jim Harbaugh for bucking the trend and making the opposing kicker more uncomfortable by not icing him.

Super Bowl Content:
The 19-3 ATS playoff record and my personal 7-0 ATS mark in published Super Bowls as a professional will be on the line for Super Bowl XLVI in Indianapolis between the New England Patriots and New York Giants. Four years ago, these two teams played in Super Bowl XLII and the Patriots were 12 point favorites. This year, the line is much tighter, likely with New England (-3), only a three point favorite, which would be the first time in 11 years and just seventh time all-time that the line has been on the magic number of three. In the previous six games though, the line has not pushed (i.e. the favorite did not win by exactly three points) and four underdogs have actually won outright. It's not my favorite line to play against, but it looks as though we will still have a strong opinion on the side.

Just as in prior years, the Super Bowl against-the-spread, over/under and straight-up pick as well as the general analysis of the game will be free to all. This content will be posted on Wednesday, January 25th at 4:00 pm ET. The boxscore, team and player props projections and in-depth props analysis as well as any updates to the game pick due to injuries or other roster information will be published on Tuesday, January 31st at 4:00 pm ET (and updated as necessary after that). Only subscribers to the Super Bowl Props with Live In-Game Super Bowl Picks package will have access to the Super Bowl content published next week. Subscribers will also have access to the live Super Bowl projection of the game, which will be great for live, by quarter or half-time wagering and will be found on the Game Picks article for the Super Bowl (I think it's pretty sweet).

College Basketball Engine Updates
As we continuously look for ways to improve our engines and leverage our TrendFinder Database information, we have uncovered and implemented a couple of different ways to enhance the college basketball Predictalator. First, we have consolidated our data so that our strength-of-schedule modifications perfectly matchup with our inputs. Ultimately a subtle change to performance, there had previously been an (unintended) opportunity for disconnect between sets of data for the same teams. This disconnect is no longer possible. Secondly, and most importantly/interestingly, we have upgraded how we handle unique homecourt advantages by college basketball school (and will do the same in the same way with college football - and all sports, though it matters the most in college sports).

Homecourt advantage in college basketball is typically presumed to mean "about four points" (the average right now is actually closer to 3.5) difference in the final score. This means that if two teams were identical, neither team would be favored on a neutral court and the home team would be favored by four if they played at one of the team's arenas. This can lead to essentially an eight point swing from one venue to the next. The truth is that some venues could actually mean up 8-9 points, while some do not help at all (and there are 345 DI college basketball stadiums). As we touched on last week, our historical accuracy in college basketball during preseason tournaments, conference tournaments and the NCAA tournament is greater than 60% ATS. However, in regular season games (i.e. where one team plays on its homecourt as opposed to the games being played on neutral courts), our performance has not nearly been as strong (this season, our picks in non-neutral games are just 51.7% ATS). In addition to the obvious difference in our performance, tracking line movements in the Play Analyzer noted lines played at the same arenas typically moved in the same direction. As we researched this further, it became clear that, aside from in the case of injuries or late-breaking roster information, line movements most often moved in the direction of homecourt (favoring a team with a strong homecourt by more or favoring the away team by more when the homecourt is not as strong). As generalized as I can state it, if the consensus for books is to flatly apply four points for the home team, those who understand the true application of home court advantages exploit that and, in turn, move the line. I do not mean to speak for books or even to say that blindly following this strategy will yield profitable results, but it did lead to some significant changes on our end as we better account for each venue's true value (relative to pace of play) both for the expectation in the game being played and in how to remove the bias of homecourt in a team's data. We have always done this to some degree, yet have drastically improved this process.

Below, we present two college basketball charts. The first chart is every DI college basketball team listed in order of the strength of its homecourt (as ranked by points per expected possession that the homecourt adds). Strong homecourts seem to fall into one of three categories: 1) High elevation (the top four arenas are all at least 4,500 feet above sea level), 2) Extreme tempo teams (teams are more comfortable dictating tempo at home so the fast teams like Missouri tend to play their fastest and the slow teams like Wisconsin tend to play their slowest at home) and 3) Average teams for their conference that still draw sizable crowds (terrible teams are terrible anywhere and great teams are usually great anywhere, but average teams with a strong fanbase, like Oklahoma State, Alabama and Eastern Michigan) have very good homecourt advantages. Remember that homecourt advantage compares expected scores at home AND on the road with no homecourt advantage factored with actual scores from games to quantify the value of playing at home. The second chart is our up-to-date against-the-spread performance for every team in college basketball. As you can see, there are a few teams that we have completely missed (like Saint Louis and Davidson), while our performance with the majority of teams has been respectable. The further we get into the season, the easier it is to incorporate this data into our engine improvements and daily picks.

Basketball Performance Notes
It's not expected to be sustainable, could be a fluke and may not have anything to do with recent tweaks, but the implementation of the updated college basketball engine completed on Saturday. On Saturday and Sunday, college basketball "normal" or better picks did go 8-2 ATS. Admittedly, performance in this area has not been as good/profitable since Week 4 (aka the conclusion of the preseason neutral court tournament period). The performance was needed. Hopefully, it is a sign that the improvements have us headed in the right direction and performance will continue to improve.

One other note of interest from college basketball is actually relevant to every sport we have tracked with the TrendFinder. It is true for college basketball, the NBA, college football and the NFL that, when we have a strong/"normal" or better O/U opinion on a game, our performance in that game overall (both O/U and ATS) is much better than the average playable pick, even if the other pick for that game is "weak." For instance, we are 17-10 ATS in games with normal+ NBA picks. Overall, in these situations, across all sports, when we have normal+ O/U picks, we are 149-110 (58% ATS). That translates to 74% ATS in NFL, 52% ATS in college football, 59% in NBA and 57% in college basketball in the most recent seasons (all better than playable picks in other situations). It would make sense that, since spreads and totals are correlated, when we think that a game is going to be played at a different pace than the line, the against-the-spread outcome is impacted by that.

Blog Topics:
With the football season winding down and the TrendFinder handling all performance reporting, even with "mad scientist mode" in full effect and plenty of new concepts for this site in creation/incubation, I will have far more time to delve deep into money-management, sports analysis and general sports (or non-sports, space-based solar power anyone?) related topics in the blog. I have several topics that I deem important that I intend to get into soon. More importantly, I would love to hear from you. If you have a topic that you would like to see me discuss/breakdown/analyze/ramble on about, please do not hesitate to contact us with your ideas or questions. Part of me writes this blog for me (though, if that were the only audience member it would probably be very different - lots of Mike Gundy bashing even if he didn't do anything "wrong" in the Fiesta Bowl), but I am far more concerned about producing (free) content that is valuable to you.

College Basketball Homecourt Advantage by Team:

College Basketball ATS Performance by Team:
Actual, real-life ATS, O/U and SU performance by team can be found on each team's schedule page. However, as we continue to leverage the TrendFinder database to find opportunities to improve our engines, we spend a considerable amount of time looking at team-by-team ATS performance of our own picks. While this information is interesting, it important to note that a) we do our best to "fix" any areas uncovered by this analysis where we believe there may be weaknesses and b) along those lines, we would not recommend blindly picking against our information when teams are involved for which we have not had a strong record this season. In the table below, "Games" represents all games that the team has played that did not result in an ATS push (including games that were not considered "playable" by the Predictalator - that info is still of great interest). "PM ATS Wins" is the number of games involving that team that resulted in an accurate ATS win by the Predictalator this year.

As usual, if you have any of your own comments about this article or suggestions about how to improve the site, please do not hesitate to contact us at any time. We respond to every support contact as quickly as we can (usually within a few hours) and are very amenable to suggestions. I firmly believe that open communication with our customers and user feedback is the best way for us to grow and provide the types of products that will maximize the experience for all. Thank you in advance for your suggestions, comments and questions.