Visualized MLB Standings, 4/13/14


The Seattle Mariners have been surprising – in a good way- so far in 2014.

The graph below plots the performance of every Major League Baseball team through games of Saturday, April 12th.  Per game averages for both runs scored (vertical) and allowed (horizontal) are included, with a positive value representing above average performance for both.  In other words, teams scoring more and allowing less than the league median can be found in the upper right, while poor teams in both areas will be in the lower left.  Run figures have been adjusted for strength of opponent.

I apologize if your favorite team is part of a jumbled mess and is indistinguishable – maximum chart dimensions are what they are.  In terms of adjusted winning % (not featured here), the two best teams early on are AL West rivals Oakland and Seattle.  I look forward to making an updated version of this graph a weekly staple here.

All raw data courtesy of

Effect of Travel on NCAA Tournament Performance

In a big way Sunday's regional final loss to Connecticut may have been out of coach Tom Izzo and Michigan State's control.

In a big way Sunday’s regional final loss to Connecticut may have been out of coach Tom Izzo and Michigan State’s control.

Traveling across the continental United States is a fact of life in late March for teams laying it on the line to potentially claim a NCAA Division I-A championship.  With all this criss-crossing the country comes the advantage one team has over the other in any given matchup regarding miles traveled.  The figures below display the average advantage, in net miles traveled, teams enjoyed over their opponent in recent NCAA tournaments:

NCAA Tourney Year Net Travel  Adv/Game (in Miles)
2011 591.5
2012 407.5
2013 612.2
2014 463.2
Average 518.9

From observing actual points scored and allowed in each tourney game in addition to expected point outputs based upon performance in regular season away and neutral site games, I attempted to quantify what this built-in advantage equated to on the court in terms of points.  Plotted in the chart below is net travel as well as actual/expected game score data for every tournament game from 2011 through Saturday’s regional final games in 2014.  Readers may filter the chart by year and tournament round at their leisure.

One way to summarize the data above is to state the percentage of teams enjoying travel advantages who ended up benefitting in terms of their actual point output exceeding their expected output; this number is approximately 59% for the years shown.

Regarding the regression lines showing the impact of travel on points scored (red) and points allowed (green), the following linear equations can be used:

Impact on Points Scored = (-0.0093 * Net Travel Miles) + 1.3

Impact on Points Allowed = (0.0092 * Net Travel Miles) – 3.2

Lets use Sunday’s East regional final, played in New York City, pitting Michigan State versus Connecticut as an example. On a truly neutral court midway between East Lansing, MI and Stoors, CT I anticipated Michigan State emerging victorious by an average of 2.8 points.
The impact on Michigan State, playing 455 miles further from home than the Huskies, would be such:

MSU Points Scored Impact = (-.0093*455) + 1.3 = -2.93 pts
MSU Points Allowed Impact = (.0092*455) – 3.2 = +0.99 pts

Based on this anticipated regression in points scored and points allowed, coupled with the positive gains made for UConn, we could have anticipated this game swinging by roughly 8 points based solely upon it being played in Manhattan, NY as opposed to a neutral location. Unsurprisingly the Huskies prevailed 60-54 to claim a Final Four spot in Arlington, TX.


Data Resources: