I don’t feel bad for adjunct professors (let the hate mail begin)
Over at Gawker, they are running a series on Adjunct professor’s, whom they refer to as “academia’s hidden underclass”. I figured I would comment because I have a blog and you can’t stop me.
- Let’s stop talking about the academic job market as if it’s one homogenous entity. Applying for academic jobs with a philosophy Ph.D. is no where near the same thing as applying for an academic job with a Ph.D. in a STEM field. And even within the STEM fields that are HUGE differences between fields. There isn’t one academic job market, there are many vastly different academic job markets and some of them are very strong markets and others are basically lottery systems where you just have to get lucky.
- If you are working as an adjunct professor and that’s your only job, you’re doing it wrong. Universities never intended for adjunct professors to make a living solely based on teaching as an adjunct. It’s not a full time job, and it shouldn’t be treated as such. The idea of an adjunct professor, in my mind, is someone that works a full time job, usually in an industry that is related to the subject they will be teaching, and they teach one class a semester, maybe two. Maybe. When I was in grad school we had an adjunct professor who worked for a major pharmaceutical company who taught clinical trials. I can assure you he wasn’t struggling to make ends meet.
- Do universities rely to heavily on adjunct lecturers. Absolutely. But why shouldn’t they? What incentive does a university have to NOT use this model? Students don’t seem to be demanding that universities use less adjuncts. (And if they did demand that, tuition would probably go up.) And it seems like there is someone always willing to be an adjunct. It would be nice to think that universities would stop relying so heavily on adjuncts because it’s the “right thing to do”, but that ignores the financial realities of running a university.
- We need to get rid of the idea that if you get a Ph.D. and don’t end up in a tenure track position that you are a failure. I think that’s why a bunch of people end up as adjuncts. They think they adjunct for a little bit and hopefully get a tenure track position later. But for a lot of people it’s just not going to happen either because the job market just isn’t there or they simply aren’t good enough. When I got my job in 2014, it was my only job offer. If I didn’t get this job I was all ready to apply for jobs in industry rather than take some temporary lecturer position. And that wouldn’t have made me a failure. It would have been fine.
- I don’t feel bad for you at all if you went and spent 10 years getting a Ph.D. in medieval studies and now you are an adjunct professor making $5000/year. I just don’t feel bad for you. And no one else should feel bad for you either. If you aren’t making enough money, then maybe it’s time to go in a different direction.
- Doing a Ph.D. is not a good financial investment. Even if you get a STEM degree, often times the bump you get from the Ph.D. isn’t enough to justify the 4-10 years in school making almost no money and possibly even going into more debt. If you are not in a STEM field, it can be a financial disaster. The ONLY reason to do a Ph.D. is if you really love researching a particular topic.
Cheers.
Data Fun with Improv and FitBit
So since January of this year I’ve been taking improv classes at Second City Training Center and last Thursday (May 12) I participated in my first “show”. It’s called Jam Sandwich, which consists of faculty and students doing improvised scenes based on several monologues. During the show I was wearing my FitBit and here is what my hear rate looked like during Jam Sandwich.
I think that huge spike around 10:40 is me after I went out for my first scene. It looks like my hear rate returned to pretty much normal as the show went on, but I was pretty nervous at the beginning. I think it’s also really interesting that my heart rate spiked again at the end of the show. That’s not something I expected.
Cheers.
The Cubs are good #hottake
This is the blog post that I would have written after the Cubs 30th game when they were 24-6 if I wasn’t in bed 20 hours a day for the last week with “the sickness”. Anyway, I scraped baseball reference to get the game results of all teams going back as far as they go. First I looked at how may teams had started 24-6 or better in their first 30 games. The list is here:
^ – Lost World Series
* – Won World Series
26-4
Detroit 1984*
25-5
Detroit 1911
Pittsburgh 1902 (No World Series in 1902)
24-6
Chicago Cubs 1907*
Pittsburgh 1921
New York Yankees 1928*
New York Yankees 1939*
Boston 1946^
New York Yankees 1958*
LA Dodgers 1977 ^
Oakland 1981
Chicago Cubs 2016 ?
(Note: Chicago White Sox 1912 (23-6-1))
Prior to the 2016 Cubs, 11 teams since 1902 have started 24-6 or better. In one of those years there was no World Series (1902), so considering the 10 teams that started 24-6 or better and there was was World Series that year, 7 of those teams made it to the World Series with 5 out of those 7 teams winning the World Series. So professionally I’m not saying that the Cubs will make the World Series, but personally I am guaranteeing in.
Next I wanted to to look at the relationship between teams winning % after 30 games and there winning % at the end of the season. The plot below shows a scatter plot of this.
No team has ever finished the season with a winning percentage over .800. The highest winning percentage ever was the 1906 Cubs with a winning percentage of 0.758 (116-36-3). More recently the Mariners in 2001 finished with a winning percentage of 0.716 (116-46).
I also fit a simple linear regression line through the data and the fitted values are as follows (red line on the scatter plot):
This model predicts that the Cubs will win, on average, 103.41 games this year based on their first 30 games with a 95% prediction interval of (84.58, 122.24). That’s probably not that interesting of an interval so I also looked at a 50% prediction interval which ended up being (96.93, 109.89). This means there is about a 50% chance that the Cubs end up with wins in this interval. Further, it also means that there is about a 1 in 4 chance the Cubs end up with fewer than 97 wins, but there is also about a 1 in 4 chance that the Cubs win MORE THAN 110 games.
Cheers.
On soccer’s declining home field advantage
As part of their final assignment in my statistics and sports class, students were tasked with looking at the home advantage in the English Premier League (EPL). In some recent and related work, James Curley and Oliver Roeder found that, by 2014, an EPL home advantage had reached an all time low.
Interestingly, that low reached new depths in 2016.
Home teams have won 40.8% of games this past year, pending this weekend’s final contests. If that mark stands, it would be the lowest in EPL/English Division 1 history, one which dates back to 1888.
Here’s a chart, similar to the one that James and Oliver produced. Overall home team win percentage in each year is shown in black, draw percentage in red, and away win percentage in green. The grey region reflects our uncertainty in the trend curve.
As we knew there’d be, it’s a fairly big drop in win percentage, from roughly 60% to 45% across about 120 seasons…
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The NFL draft – where we stand in 2016
Another NFL draft has come and gone, and with it has come the predictable displays of unyielding optimism, stale and arguably race-based generalizations of player skill, and, as a relative newcomer in 2016, lazy misuse of the term analytics.
In following along this spring, it became clear that what is mainstream knowledge among researchers is far from it in the national media. This despite a decent amount of both academic and non-academic research into the topic.
For those new to the scene, or even for a few veterans who may have missed an article or two along the way, I decided to write a quick review of what’s out there. Note that many of the following points are related to one another.
1. Top draft picks are overvalued.
In an efficient market, the value of picks traded between teams would be equivalent. That is, if Pick X was traded for Picks Y and Z…
View original post 1,025 more words
JSM2016 Data Art Show
JSM 2016 will host a Data Art show for the first time at this year’s meeting in Chicago. If you are interested in submitting a piece, please follow the instructions here. Everyone who is interested should submit something for acceptance. And remember “I’m not an artist” is a bad excuse.
If you have any questions to can email artjsm2016@gmail.com
Cheers.
Stats in the Wild 