Staying Power of the Dominant Pitcher Starts with Keeping the Bases Clear, Not Power Outage


There’s a perception that baseball’s drop in scoring this decade has something to do with MLB eliminating performance-enhancing drugs and power numbers dropping. It’s a logically and statistically supported thesis, but the assessment is ankle deep.

A more complete explanation lies somewhere between the knees and the bottom of the letters, where pitchers are pounding the strike zone at rates unseen since the early 1920s. Batters seem to know this because they’re taking fewer pitches, but that isn’t helping them get on base or ultimately get runs across the plate, and pitchers are facing fewer hitters than they have since the pitcher-dominant 1960s and 70s. If 1968 was the Year of the Pitcher, 2015 might be the year those on the hill authoritatively regain the advantage stripped from them as a result of that mound-altering season.

Across baseball, teams are scoring 4.11 runs per game, which after 2014’s 4.07 average is the lowest since 1981 (4.00). ERA is at 3.82 this season, which aside from last year’s 3.74 mark is the lowest since 1992 (also 3.74). We only have to go back to 2012 to find one of the 50 season ERA marks north of four (4.01) out of 140 years on record, so this hasn’t been such a gradual slide. But runs per game and ERA leave plenty of wiggle room for how offense and pitching are evaluated.

Oddly enough, one of the most reliable numbers for appraising batting might actually be partly to blame for common opinion on the shift in the game. OPS – a number commonly highlighting power hitters – is down to .712 this season, which is the second-lowest mark since 1992 (.700). But OPS stands for on-base plus slugging percentage. We need to separate the two to identify the more impactful ingredient causing this season’s particular shift.

Slugging is at .397, which in the context of the post-1994-95 strike is low. But it’s also up 11 points from last season, which accounts for nearly all of the 12-point rise in OPS from 2014.

Part of the problem is we often use the 1994-95 work stoppage as the cutoff for assessing offense. It’s easy to fall into this since those seasons are commonly associated with a shift in the game toward an era of gluttonous offense as the aesthetic of hitters ballooned to cartoonish proportions, but doing so probably limits the ways in which we can properly quantify meaningful trends in the way the game has been and is being played.

The other cutoff for these assessments is the lowering of the mound in 1969. When numbers from 1969-94 are included, present-day slugging becomes middling.

It would follow that home runs this season (0.95) rank 20th among 47 seasons since the 1968 Year of the Pitcher, which might not be as drastic of a dip as some expect. And home runs are up markedly this season from 0.86 last year, which signals something else is going on.

The middling that happens with slugging when expanding the era from 1995 to 1969 doesn’t occur with the other half of OPS. With on-base percentage, the cellar from the 1995-2015 chart is about the same as the cellar from 1969-2015 with 1972 as the lone exception.

So it seems not power but on-base percentage could be the greater cause for offense falling off. Pitchers aren’t allowing runners to reach base. Slugging, while not approaching the levels of the late 90s/early 2000s, is still well ahead of the average rate from 1969 until 1990. But more often than all but one season since the physical makeup of the field was changed to give hitters more of an advantage, pitchers are keeping the bases clear for when those run-scoring threats occur.

OBP, of course, can also be further parsed. Hitting is a major part of it. Batting average is at .253, which is tied with 2013 for fifth-lowest in the 1969-2015 era.

But looking at walks is where the most significant shift happens and should make us finally give more credit to pitchers’ raw abilities rather than passing the evolution off as Major League Baseball’s alleged eradication of synthetic hitters. This season’s rates of walks per nine innings (2.84) and walks per batter faced (.075) are respectively the lowest since 1921 (2.79) and 1922 (also .075). This season’s WHIP is 1.28, which would match last season for second behind 1972 (1.26) in the 47 seasons beginning in 1969.

With that information, it’s not immediately surprising pitchers are throwing fewer pitches per inning (16.0) than any season since 1993 (15.8). But in an era where 95 mph fastballs, devastating curves and wipeout sliders are the norm, they’re still striking out 7.64 batters per nine innings – a rate only bettered in 2014.

It follows that the strikeout-to-walk rate is at an all-time high for the era of the lowered mound, and strikeouts per batter faced (.202) trails only last season. Through 2009, the highest K-to-BB ratio in baseball history was the 2.09 pitchers registered in 1968. That was bested with a 2.17 in 2010, and it’s taken a stark climb ever since.

Granted, hitting, fielding and ballparks have also evolved in that time, and it’s irresponsible to dub 2015 the true Year of the Pitcher. But in the 28 seasons of available pitch-count data, hitters are actually taking fewer pitches (52.9 percent) this season than any other besides 1988 (also 52.9), and they’re putting the ball in play (18.8 percent of pitches) at levels lower than every season but 2012-14 (18.6 in each). The percentage of swings put in play (39.9) is the lowest on record from those 28 available seasons.

Though pitches per inning are at low levels, pitches per batter faced is up to 3.81 – the seventh-highest total in 28 years – which isn’t all that surprising considering the high strikeout rate. But if pitches per inning is down and pitches per batter faced is up, it follows that pitchers are facing fewer batters per inning. In fact, in data available back to 1921, the 4.21 rate this season is the lowest since 1972 (4.18) and better than all but six seasons with 1968’s 4.13 mark leading the way. The average has actually dropped in nine straight seasons from 4.35 in 2006. The end result has been a season of pitching efficiency that’s difficult to rival.

Even so, the timing of the shift can’t be ignored. Maybe the shift in clubhouse culture has had something of a weighted donut effect for pitchers. Maybe a decade of hitters in the gym with bags full of synthetic helpers conditioned pitchers to better handle the more even playing field the game alleges to have now. Maybe in 1968 pitchers and hitters were on an equal physical plane with pitchers having a boost from the heightened mound. In the 1990s there was an influx of hitters with a reputation for tipping the physical advantage in their favor with PEDs. In the 2010s that physical gain has been to an extent scared out of the game.

Maybe things are equal again, and after the evolution pitchers are having an easier time getting batters out because of all the symbolic heavy lifting they had to do in the 1990s while hitters were doing synthetic heavy lifting—a decade or so of essentially pitching with a donut around the pitching arm. Maybe the donut has been gently tapped off after a bullpen session rather than in the on-deck circle and the numbers we’re seeing are the result.

It took 47 years, but pitchers are in more of a position of power than they’ve been since they had that grand mound to stand on.

Fantasy Sports: Why You Should Be Making Them a Reality in Your Brand Activation


Fantasy sports have typically been reserved for one group—older, high-income, male sport enthusiasts. Leading brands targeting this group, such as Dove Men + Care, Snickers, Verizon and Toyota, have all invested heavily in leading fantasy destinations and their own activations.

However, fantasy sports’ appeal is much broader and will continue to expand in the future. This presents an exciting opportunity for brands looking to be first to engage non-traditional players through a proven platform. Consider the following:

  • Fantasy is for teens

In the United States, almost one in three teens (age 12–17) play fantasy sports. This is the fastest growing segment of all fantasy players, up 75 percent in new players from last year. By comparison, 41 percent of teens have claimed to use Snapchat, so fantasy is not far behind. Now only imagine fantasy on Snapchat…mind blown!

  • Fantasy is for women

Fantasy sports added 6.8 million new female players since 2013, approximately 2 million more than male players. Not only are women outpacing men, but more women are choosing fantasy sports over other more “traditional” programming. For example, nearly 3 times the amount of women started playing fantasy than watched The Bachelorette (2.5 million).

  • Fantasy is for other “sports”

Fantasy sports can enhance viewing of forms of entertainment like events, shows or movies. For example, Matthew Berry, who is the face of fantasy sports for ESPN, recently founded a fantasy film game, Fantasy Movie League ( In his game, instead of picking between Tom Brady and Aaron Rodgers, you’re selecting between Jurassic World and Pitch Perfect 2. Fat Amy over Chris Pratt every day in my book.

  • Fantasy is for good

Pledger is a start-up that allows fans to make a monetary contribution to an athlete’s causes based on his or her on-field performance. Think the jog-a-thon from grade school, but way cooler. If your brand sponsors an athlete, this could be a great way to reinforce your athlete relationship while making a positive contribution to society.

No matter your target consumer, with a fantasy game in your next campaign, you can increase the chances of making fan engagement a reality for your brand.



Is there a relationship between recent team performance and in-stadium attendance?


It is expected that when home teams are on winning streaks, more fans would want to see their team. Thus it would make sense to promote that success to lead to higher in-stadium attendance. But is the data really consistent with that assumption? To test this out, we can compare MLB attendance data against the season average when home teams are on a winning streak. For example, in 2014 there were two games where the home team came into the game with a 10-game win streak. First was the Friday, August 22, 2014 game with the Washington Nationals where their attendance was 33,718. Second was the Sunday, September 14, 2014 Los Angeles Angels game where the attendance was 35,364. The Nationals 2014 average attendance was 31,844, so that game was a bit over their average. The Angels 2014 average attendance was 38,221, so their game was a bit under their average.

2012   2013   2014   2015  
Avg. Incr. Streak Avg. Incr. Streak Avg. Incr. Streak Avg. Incr. Streak
-0.71% 3 0.24% 3 1.82% 3 2.64% 3
-1.47% 4 -3.98% 4 4.03% 4 2.42% 4
-1.14% 5 -4.08% 5 7.64% 5 2.92% 5
5.13% 6 -1.23% 6 3.28% 6 -0.60% 6
-8.14% 7 0.86% 7 7.37% 7 16.79% 7
-3.21% 8 6.66% 8 -2.45% 8 -13.14% 8
0.99% 9 21.61% 9 -6.07% 9 -12.44% 9
-4.32% 10 8.77% 10 -0.80% 10 -22.97% 10

The increase or decrease in average attendance can fluctuate quite a bit regardless of whether a team is on a winning streak. What could account for these frustratingly inconsistent results? The data from what day the game occurs on might give us a better indicator:

2012   2013   2014   2015  
Avg. Incr. Day Avg. Incr. Day Avg. Incr. Day Avg. Incr. Day
-11.40% MONDAY -9.03% MONDAY -9.52% MONDAY -6.95% MONDAY
-11.43% TUESDAY -11.81% TUESDAY -10.48% TUESDAY -14.52% TUESDAY
9.30% FRIDAY 6.33% FRIDAY 6.97% FRIDAY 8.41% FRIDAY
7.06% SUNDAY 9.46% SUNDAY 8.30% SUNDAY 12.21% SUNDAY

Now this data shows a more consistent, logical result. Average attendance increases much more on the weekends than during the week. Because the weekends tend to draw significantly more fans, a winning streak for a game on a Tuesday might not increase attendance enough over the season average to give a positive result.

But what if we combine these two results to look at the average increase over the average attendance on that specific day during a win streak? In other words, does a Tuesday game during a winning streak increase over the average attendance of Tuesday games?

2012   2013   2014   2015  
Avg. Incr. Streak Avg. Incr. Streak Avg. Incr. Streak Avg. Incr. Streak
-1.26% 3 -0.26% 3 1.37% 3 -0.74% 3
-2.54% 4 -1.20% 4 4.50% 4 0.21% 4
-0.93% 5 -1.92% 5 5.85% 5 1.19% 5
5.69% 6 -0.65% 6 3.05% 6 2.30% 6
-3.09% 7 5.53% 7 4.18% 7 15.95% 7
-3.93% 8 6.15% 8 -6.05% 8 -13.62% 8
16.22% 9 16.18% 9 -6.69% 9 -7.75% 9
10.16% 10 11.23% 10 -1.28% 10 -14.42% 10

Again, the inconsistent data indicates that winning streaks don’t seem to affect in-stadium attendance. To go back to our example above, the Nationals averaged 33,679 fans for Friday games, which the attendance was only slightly above. The Angels averaged 36,333 fans for Sunday games, which the attendance was still below. So when allocating marketing resources, winning streaks may not push fans into the stadium as commonly thought.

By: Zax Foster, Sr. Developer

Push vs. Pull? Let Us Help You!


by Bill Plummer, Senior Project Manager

Some of our direct-feed clients prefer a “pull” delivery method, whether it’s FTP pull, http get or our RESTful API. The challenge with a pull delivery method is that it forces you, the client, to apply business rules to your pull frequency in order to be as efficient as possible. These rules can sometimes be complicated in nature, especially if you receive multiple sports or various types of data. It’s also possible your in-house or third-party development team may not watch or understand all the nuances and expectations of the 500+ leagues that we cover. Inefficient pull frequencies can end up affecting your bottom line—if you aren’t optimizing according to the sports you are developing for, you are costing yourself time and money in bandwidth, processing power, speed and timeliness.

Luckily, there are two great solutions to this problem. First, for every pull feed we have, we have a push feed equivalent. XML FTP, JSON HTTP post (sourced via our API), socket, custom files, etc.—it’s the identical structure and content you pull from us now, but available via push—and at the perfect frequency. As soon as a play is updated, we push out the data. If the game was yesterday, but there is a stat change, we push out the data. No matter how a sport works or the frequency of a certain type of data, we know when to push it to you. Save yourself time, money and effort, and let us push the data to you.

Second, if you do prefer pull, your dedicated client manager is here to help. Our client managers are well-versed in the behaviors and update frequencies of the feeds we offer for the sports we cover. They are at your disposal and are eager to answer all of your technical questions, so by all means run your frequency and timing plan by them. If necessary, ask them to sit in on calls with your developers. We’ve built information in our feeds so that the proper frequency can be dynamically coded on your end; if your developers aren’t properly utilizing that, our client managers can help them.

Learn more about our sports data feed/API.

Take Advantage of our New Touch Points for Soccer


Data in football continues to grow due to an increased appetite from fans for deeper insights. Easy access to material via the internet also makes consumers increasingly sophisticated, which in turn creates a need for more intelligently presented information and analysis.

Recognising this fact, STATS has enhanced its live sports data APIs/data feeds with numerous new event data points for every match across a number of competitions, including the English Premier League, German Bundesliga, Italian Serie A, Spanish La Liga and the UEFA Champions League.

Along with usual scoring, shooting, set piece and save data, creative statistics such as shots created and second assists have been added, alongside passing breakdowns, defensive actions, aerial and ground duels as well as other possession-related data, including estimated possessions advanced in metres and all player touches. All new event data points are timestamped with XY pitch coordinates as well as being broken down in to defensive, middle and attacking thirds of the field, with outside and inside the box breakdowns for shots. All statistics are provided both on a match-by-match and cumulative over the course of a season basis.

The STATS passing data tracks link up play between players, identifying which players connected the most often, both in terms of passes given and received to view a team’s or player’s entire passing network.  There are also descriptive events, from pass directions to cross, assist and shot types, going beyond the raw data point to  describe the action, whether than is a forward pass, an outswinging cross, a penetrative pass that led to an attempt or a lobbed finish.

STATS can also aid with the analysis, providing anything from insights into each game to in-depth analytical research, enhancing the opportunities offered alongside its new soccer event data points.

By: Jimmy Coverdale, Soccer Analyst