Like most Chicago football fans, I was pretty distraught after the Bears lost last Sunday’s playoff game courtesy of a missed field goal at the end — a kick that first hit the goalpost and then the crossbar before ultimately failing miserably. While most local fans were grief-stricken like me, some were irrationally inconsolable, demanding the scalp, and worse, of the Chicago kicker.<\/p>\n Ever the stats and randomness guy, I tried to talk my rabid friends off the ledge — with little success. The 40+ yard attempt was certainly not a gimme, even for the very best kickers, I opined. What if the gods had allowed the kick to bounce through; would the kicker have then been a hero? Why not blame the defense for allowing the late touchdown? Even with the final miss, the Bears kicker made 3 or 4 attempts for the game — certainly a credible day’s work. Would there be such venom if he’d missed the kick with 5 minutes left rather than 5 seconds? Alas, it seemed there was no convincing the hard core, especially regarding a guy who’ll almost assuredly not be with the team next year.<\/p>\n The kicker hit on 77% of his attempts this season and has made 84% over his career. These numbers compare favorably with the much-beloved kicker of the Super Bowl-winning team of 1986. Indeed the 1986 kicker made just 50% of attempts in the 40-49 yard range that season, and only slightly more than 50% of such attempts for his career. So, he’d have been just 50-50 to make the same kick this year’s guy missed.<\/p>\n Comparing kickers of different eras is tricky though. Performance and hence standards change over time. What was “par” 30 years ago may be bogey today.<\/p>\n What’s a grief-stricken, data-driven Bears fan to do? Find data to analyze of course. It didn’t take long to get started.\u00a0<\/span>ESPN<\/a>\u00a0<\/span>has clean field goal numbers from 2002-2018 by team and kick length category. Though I’d ultimately like to dig back even further in NFL history, I thought this’d be a convenient point of departure. The remainder of this article revolves on scraping and wrangling the ESPN data, followed by several visuals that show 17 year trends.<\/p>\n For the analysis that follows, the technology used is JupyterLab 0.32.1 with Microsoft Open R 3.4.4. As always, the R work is driven by the data.table package and tidyverse ecosystem.<\/p>\n Read the entire blog\u00a0here.<\/a><\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<\/a><\/p>\n<\/p>\n