Long before the COVID-19 pandemic shutdown Broadway, the industry ran 8 shows /week x 52 weeks a year. Standardized state-of-the-business reporting filled executive inboxes every Monday and were (are?) guages for external rankings, water-cooler analysis, and internal projections. During the summer of 2017, Tim Raines entered the National Baseball Hall of Fame with an impressive resume that seemingly lost without advance data analysis (including terrific reporting by Jonah Keri.) That same summer I drew a parallel to Broadway data and how in this new age of big data, this industry should get to know Tim Raines….
From summer 2017:
We live in a power ranked world.
What was the #1 movie last week? Who’s the best NFL team? Hey Yelp, what’s the best Mexican cuisine in my zip code? Whether we like it or not, we love to rank.
Broadway is no exception. Like clockwork, weekly ticket grosses are distributed on Monday afternoon, at approximately 15 hundred hours. Rankings begin immediately, starting with a list of top overall revenue achievers. Other analysis highlights what shows reached the Million Dollar Club? What show had the highest average ticket price? Did anyone beat Hamilton this week? Who’s at the bottom?
As a kid, I collected baseball cards. Hundreds and hundreds of baseball cards. When I was 10ish, baseball cards were baseball’s annual delivery of a single player’s 10–15 baseline stats. By the transitive power, a players trading card became more valuable should he become a star, most notably their rookie season card. Baseball cards were, in essence, how we measured controlled statistics of player performance. Any card I could get my hands on was hoarded in hopes that card would become a sellable commodity 5x as much as the original card. Depreciated stats of the real-life player quickly turned their card into a “spokecessory”, unless it had the decade-defining awesomeness of say, this. Not trading a Paul Coleman rookie card was a strong argument a 10ish year old once lead. And this dude was supposed to be the Johnny Bench of the 1990s.
Roughly a decade ago, with the birth of “Moneyball”, Sabermetrics became a more in-depth focal point for front-office executives in player’s performance evaluation. In short, the term sabermetrics is defined as a deeper dive into empirical analysis of baseball, measuring more ‘in-game’ statistics to gauge performance. Data is micro computed against the most detailed of variables. How well does a player hit on 3–2 counts, against a breaking ball, in the rain, on a Sunday, on the West Coast, during a day game, prior to Mother’s Day? Ok, that example measures many independent variables, but you get the point. Simple back-of-the-baseball-card statistics were expanded to address these variables, historically and present-day, and measure success against them.
Tim Raines is arguably one of the greatest baseball players. Yet, it took until his 10th and final year of eligibility to be elected to the baseball Hall of Fame. Along with Andre Dawson and Gary Carter, Tim Raines personified the 1980s Montreal Expos. One might even say Tim WAS/IS the quintessential Montreal Expo as the other two gained greater fame elsewhere. Ultimately, baseball couldn’t survive in Montreal, and like the Expos, Tim “The Rock” Raines moved elsewhere.
Tim Raines ended his career with a very solid .264 batting average, 2,605 hits, 140 home runs, and trademark 808 stolen bases. He was a 7-time all-star (all with Montreal) and eventually won 3 World Series Rings (in late career, limited roles with the Yankees and White Sox of the late 90s/2000s.)
The Baseball Hall of Fame has no automatic entry benchmarks — sportswriters are allowed to vote 10 candidates per year. Those that reach 75% of the vote get in. While no formal prerequisites apply to merit induction, a few entry-level benchmarks exist that deem a player HOF worthy: any solid combination of .300 batting average, 3,000 hits, and 500 home runs is usually grounds for entry. If you hit the trifecta or offer some other impressive career census, you’re usually elected in your first year of eligibility. As you see from the back-of-the-baseball-card data above, Tim Raines doesn’t necessarily qualify. Moreover, Tim Raines played his entire career at the same time (and in the metaphorical shadow) of Rickey Henderson, the greatest leadoff hitter and stolen base leader of all time. Defining skill sets for Tim Raines overshadowed by a statistically husky peer. For 10 years, Tim Raines was buried on terrible Expos teams, in a terrible stadium, while the running Rickey juggernaut was making headlines on World Series teams in Oakland and New York. You could even make an argument at the time he wasn’t even the best player on his team (see Dawson and Carter.) Most important, yes. Best? Debatable.
On those numbers and intuition alone, Tim Raines doesn’t seem HOF worthy. But then something funny happened on the way to Cooperstown. The electorate changed. Sabermetrics became a thing, and a terrific deeper dive into Tim Raines’ career by Jonah Keri, revealed a truth no kid of the 80s could read on the back of a baseball card — his career exceeded many of his fraternal members.
He reached base 3,977 times — more than 8-current Hall of Famers. Though he’s 600+ stolen bases behind his arch nemesis (and all-time leader) Henderson, Raines’ 808 stolen bases are 5th in MLB history. All 4 people in front of him are Hall of Famers. His rookie season of 1981 was marred by a 2-month strike. Still, he managed to swipe 51 bases in the first 54 games BEFORE the strike! His offensive stats were 43% above-league-average during his playing years, and some of those stats are new-age stats not measured until the start of the 21st century. Another new-age stat that summarizes a player’s contribution to their team — a schmancy term called ‘wins-above-replacement’ — puts Tim Raines as the best player in the MLB during a 5-year period 1983–1987. Again, another stat not promoted in his playing days and another stat elusive from a baseball card but relative to today’s analytic world. As more and more of these stats were derived and delivered, Tim Raines’ yearly Hall of Fame voting totals increased until the data-driven electorate Kramered the Hall of Fame doors for him yesterday.
Said quite simply, a more advanced analytic ecosystem uncovered the true value of Tim Raines.
The same value analysis should be determined for Broadway and all performing arts organizations. Monday afternoon grosses highlight back-of-the-baseball card data necessary to the 140-character gossipy power ranking, but may not necessarily dive into true sabermetrics of institutional performance. As I work with Broadway shows and performing arts organizations, we’re diving into larger forensic inspection not currently visible on the B-side of a card. Are you defining show-centric, patron-centric, or brand-specific KPIs that meet organizational goals? A touristy musical should have different KPIs against the control group than a limited run play. In the theatre game, per capita ratios, transactional velocity cataloging, and point-of-sale/scale-of-house indexes are examples of strong deliverables to measure KPIs. Same holds true for operas and ballets. More importantly, are you creating KPIs that meet your weekly, monthly, or yearly goals? Sure, you might be the #5 show in overall gross, but #2 or higher in orchestra P1 sales velocity. If you’re implementing variable pricing strategies, are you also implementing variable KPI standards? Are your marketing efforts segmented to properly address those KPIs? * How are loyalists measured against same or differing KPIs?
As Tim Raines can attest, back-of-the-baseball card data only gets you so far. Create smarter, hyper-targeted KPIs for every decision you make, that’s the true meaning of analytics. Sabermetrics helped baseball fully digest the true value of a potential Hall of Famer. Uncovering your own data nuggets will paint a forward thinking picture for goal setting and start to write the story of how you can improve or optimize performance.
Who cares if you’re not number 1 or riding in the shadow of a juggernaut? Define your own KPIs and run the bases against those variables. Doing so defines your true measure of success.