What states/regions have outperformed or underperformed compared to my projections the last couple of years?
First, for a valid look into this topic, you have to consider that there are some teams that simply run very well (or very poorly) at NXN. To this end, I took out any outliers by comparing how teams scored vs. projections from both myself and Meylan. If both Meylan and I were off (in either direction) by 25% or more, I considered those as outliers. That ended up with 14 outliers which you can see at the bottom of this post. Of the remaining 74 teams, I used a simple criteria of average score vs. average projected score.
Quick summary: There was one region that I have apparently overestimated (the South), and I probably underestimated the California girls in 2013. Two other regions were harder to read, and I think the simplest explanation is that both Meylan and I have underestimated one of those regions (the Heartland), and that the Northeast we have both overestimated simply because of a couple teams running a little off and skewing the data. As for the Heartland, I guess it is also possible that nearly half of those teams have simply had big days (Wayzata girls 2013 and boys 2014, and Edina boys both years) since the other five teams all ran pretty close to expectations, but when almost half of the teams fall in the category of “outliers” something (in this case, our projections) is almost surely off. The Northeast is a harder read because most of the region has been very close to projections, but a couple of states (NH and PA) have been missing the mark for whatever reason.
Anyway, here is a pair of charts. The first one deals with average scores WITHOUT the outliers included, while the second one deals with ALL teams regardless of whether or not they appear to be outliers. Red means potentially underrated, and yellow means potentially overrated.
Non-Outliers Only:
Actual |
Watchout |
Meylan |
Watchout |
Meylan |
# |
|
282.10 |
297.80 |
287.30 |
CA |
105.6% |
101.8% |
10 |
348.60 |
359.00 |
352.80 |
HL |
103.0% |
101.2% |
5 |
260.78 |
263.78 |
254.11 |
MW |
101.2% |
97.4% |
9 |
261.72 |
240.72 |
245.67 |
NE |
92.0% |
93.9% |
18 |
326.38 |
312.25 |
294.38 |
NW |
95.7% |
90.2% |
8 |
318.50 |
324.88 |
312.25 |
SE |
102.0% |
98.0% |
8 |
373.63 |
330.13 |
404.38 |
SO |
88.4% |
108.2% |
8 |
253.00 |
255.38 |
251.25 |
SW |
100.9% |
99.3% |
8 |
Outliers Included:
Actual |
Watchout |
Meylan |
Watchout |
Meylan |
# |
|
281.55 |
287.73 |
275.27 |
CA |
102.2% |
97.8% |
11 |
281.67 |
349.33 |
334.33 |
HL |
124.0% |
118.7% |
9 |
280.50 |
273.60 |
264.80 |
MW |
97.5% |
94.4% |
10 |
271.43 |
234.48 |
237.67 |
NE |
86.4% |
87.6% |
21 |
309.89 |
306.22 |
288.56 |
NW |
98.8% |
93.1% |
9 |
318.50 |
324.88 |
312.25 |
SE |
102.0% |
98.0% |
8 |
392.56 |
329.22 |
406.00 |
SO |
83.9% |
103.4% |
9 |
256.55 |
259.73 |
258.91 |
SW |
101.2% |
100.9% |
11 |
And here is a chart including all the teams. The first number is their actual score, the second is my projection, and the third is Meylan’s. Boys on the left and Girls on the right.
2014:
111 |
Fayetteville-Manlius NY |
171 |
139 |
70 |
Fayetteville-Manlius NY |
82 |
145 |
|
159 |
Wayzata MN |
305 |
260 |
149 |
Great Oak CA |
217 |
180 |
|
178 |
North Central WA |
258 |
242 |
173 |
Carmel IN |
206 |
179 |
|
191 |
Liverpool NY |
243 |
225 |
198 |
Naperville North IL |
237 |
216 |
|
195 |
American Fork UT |
160 |
144 |
199 |
Desert Vista AZ |
314 |
330 |
|
200 |
Davis UT |
202 |
193 |
240 |
Wayzata MN |
256 |
242 |
|
224 |
Ventura CA |
230 |
263 |
260 |
Blacksburg VA |
259 |
230 |
|
251 |
St. Anthony's NY |
229 |
180 |
272 |
Lewisville Hebron TX |
282 |
375 |
|
262 |
Edina MN |
417 |
418 |
276 |
La Salle Academy RI |
332 |
308 |
|
269 |
Jurupa Hills CA |
265 |
289 |
278 |
Saratoga Springs NY |
201 |
225 |
|
271 |
Christian Brothers NJ |
234 |
240 |
294 |
Saugus CA |
256 |
310 |
|
285 |
Timpanogos UT |
286 |
315 |
314 |
Palatine IL |
256 |
220 |
|
299 |
Severna Park MD |
332 |
340 |
337 |
Camas WA |
341 |
295 |
|
302 |
Daniel Boone TN |
314 |
350 |
342 |
Willmar MN |
355 |
390 |
|
328 |
Sandburg IL |
288 |
237 |
347 |
Davis UT |
350 |
335 |
|
363 |
Carmel IN |
301 |
330 |
360 |
Coe-Brown Academy NH |
296 |
322 |
|
368 |
Summit OR |
322 |
331 |
372 |
Unionville PA |
266 |
302 |
|
372 |
La Salle Academy RI |
205 |
179 |
380 |
Shenendehowa NY |
300 |
323 |
|
384 |
Southlake Carroll TX |
313 |
394 |
383 |
Green Hope NC |
291 |
336 |
|
399 |
Brea Olinda CA |
322 |
400 |
390 |
Coeur d'Alene ID |
364 |
375 |
|
409 |
Sioux Falls Lincoln SD |
392 |
392 |
427 |
Lewisville Marcus TX |
314 |
389 |
|
544 |
The Woodlands TX |
322 |
419 |
442 |
American Fork UT |
287 |
295 |
2013:
111 |
Gig Harbor WA |
173 |
140 |
108 |
Wayzata MN |
221 |
202 |
|
139 |
Christian Brothers NJ |
172 |
139 |
120 |
Fayetteville-Manlius NY |
128 |
135 |
|
174 |
Brentwood TN |
234 |
230 |
157 |
Davis UT |
213 |
213 |
|
216 |
Fayetteville-Manlius NY |
139 |
145 |
182 |
Carmel IN |
203 |
200 |
|
225 |
Carmel IN |
276 |
288 |
211 |
Monarch CO |
215 |
232 |
|
228 |
St. Xavier OH |
251 |
262 |
212 |
Fort Collins CO |
205 |
212 |
|
230 |
American Fork UT |
226 |
199 |
214 |
Unionville PA |
197 |
201 |
|
231 |
North Central WA |
240 |
219 |
276 |
Great Oak CA |
187 |
155 |
|
255 |
Arcadia CA |
234 |
190 |
277 |
Simi Valley CA |
362 |
285 |
|
263 |
Edina MN |
406 |
365 |
278 |
Bozeman MT |
278 |
238 |
|
299 |
Northport NY |
271 |
289 |
279 |
Pennsbury PA |
222 |
271 |
|
301 |
WC Henderson PA |
274 |
287 |
282 |
Davis Senior CA |
368 |
332 |
|
303 |
Severna Park MD |
338 |
314 |
336 |
Naperville North IL |
356 |
355 |
|
308 |
Southlake Carroll TX |
269 |
385 |
352 |
New Braunfels TX |
333 |
372 |
|
322 |
Wayzata MN |
377 |
341 |
373 |
Saratoga Springs NY |
370 |
365 |
|
325 |
Madera South CA |
363 |
298 |
376 |
Assumption KY |
389 |
327 |
|
344 |
Davis UT |
399 |
380 |
398 |
Southlake Carroll TX |
315 |
385 |
|
347 |
Dana Hills CA |
361 |
326 |
426 |
Coe-Brown Academy NH |
345 |
326 |
|
375 |
Central Catholic OR |
346 |
337 |
430 |
East Ridge MN |
415 |
399 |
|
401 |
Liverpool NY |
247 |
245 |
433 |
The Woodlands TX |
372 |
425 |
|
415 |
Lewisville Hebron TX |
443 |
510 |
451 |
Blacksburg VA |
442 |
371 |
|
458 |
Hinsdale Central IL |
362 |
361 |
521 |
Bellarmine Prep WA |
434 |
420 |
Team Outliers the last 2 years at NXN =
2014 Boys
Exceeded (3): Wayzata MN, North Central WA, Edina MN
Fell short (2): La Salle Academy RI, The Woodlands TX
2014 Girls
Exceeded (1): Desert Vista AZ
Fell short (1): American Fork UT
2013 Boys
Exceeded (1): Edina MN
Fell short (3): Fayetteville-Manlius NY, Liverpool NY, Hinsdale Central IL
2013 Girls
Exceeded (2): Wayzata MN, Davis UT
Fell short (1): Great Oak CA
This is a great. Wouldn't this be a much richer analysis if you did the same thing, not on team score, but on each individual team runner's final speed rating (or place) relative to your's and Meylan's predictions? This could help to separate out those teams where you prognosticators truly missed the mark with your predictions from those teams who truly underperformed due to one or more runners having a bad day and killing the team score. Your method right not does not discriminate between those two scenarios.
ReplyDeleteSecond, notice that I only talk about underperforming relative to ability. This is because, by definition, a runner can't overperform (i.e. you can't do better than your ability, although you can "peak" of course for an important race, which would look like a slight "overperformance"). For your analysis, I think you should incorporate this concept. In a measured athletic endeavor, the distribution based on one's ability is not a bell curve! So there should be no outliers above, only below (bad race). Any outliers above are by definition (almost) underprediction.
Love your work. Please don't take this as a criticism. I just would love to see the outcome of your analysis above with these two concepts included.
I did something like that in 2013 in my NXN recap (http://www.dyestat.com/gprofile.php?mgroup_id=44531&do=news&news_id=198252), although not quite at that level (I was just taking out highlights to help tell the story).
DeleteThough I would agree that performance distribution isn't a bell curve, I would disagree that you can't overperform, because this perception is based on observation and projection based on what the athletes have done before, not what they are capable of (which no one really knows). It is absolutely possible to exceed expectations, which is what those outliers are discussing: you run better than you did in previous races (either your best race, or better than your average race).
I think that those outliers that were exceeding expectations fall into three groups:
1. Those that were underrated going into the meet because we didn't have as good of a read on them
2. Those that had setbacks in previous races (off races, injured or sick athletes, etc.) that lowered our expectations
3. Teams that ran at or near their best moreso than the rest of the field (on average).
Oh, and I also posted a shorter review after NXN last year on the message boards: http://www.runnerspace.com/forum/index.php?showtopic=1130392
DeleteThanks for your replies. Sorry if I wasn't clear about a point I was trying to make. I'm talking about ability, not expectation or prediction. As a result, I would not treat outliers above and below the same in your analysis, and again, I'm referring to invidual runners, not teams.
DeleteYou can overperform relative to expectation, or history, but by definition you can't overperform relative to ability. There's a difference and it suggests that we should treat outliers above one's speed rating different than outliers below a speed rating. If you performed better than you ever did before, this new performance is your ability. The question, perhaps, is how much is a speed rating (composite) a measure of one's ability? I realize of course that it is a measure of his prior performances relative to other runners.
I think the issue is that running better than a prediction is qualitatively not the opposite of running worse than a prediction. While healing from a prior injury or sickness, deliberately holding back, or good peaking can vault a runner well above their composite speed ratings, what other reasons could there be why a runner would exceed his composite speed rating if that speed rating is a measure of his ability? Conversely, there are millions of reasons why you can run worse than your speed rating.
I realize a speed rating is not necessarily a measure of one's ability, but I think it would add an interesting dimension to your analysis if you treated it that way, by handling outliers above and below differently.