# Making up things and supporting with faulty analysis

Update 7/11/202: I took a harsher stance against Mr. Jamison’s article. As I communicate with him over email and see his willingness to share data and refine his model I see my comments as little harsh. Instead of updating them I will leave them for what they are so you can also judge my writing.

How do VCs decide to pass on an startup? If you were to read a TechCrunch article you will find a quantitative model supported by statistical analysis:

Likelihood of Receiving Term Sheet = -0.355  +
0.349 (Team) +
0.334 (Market) +
0.222 (Traction) +
0.029 (Product)

A nice linear regression model with an R2 value of 0.5 that states Likelihood of getting Term Sheet as a function of four attributes. This article and the regression model comes from a partner in a VC firm, Mr. Jay Jamison.

Sounds plausible?  Fits your notion that VCs invest in teams and not product? Is the fact that this is a regression analysis done by a VC partner enough to convince you to suspend your disbelief and accept this predictive model at face value? Or are you going to walk up to the stage and tell the magician that you are not satisfied with his lift shuffle and you are going to do it yourself?

Let us do the latter and while we are up on the stage let us ask the magician to roll up the sleeves as well.

How did he build the model? Mr. Jamison, the author, said he rated each pitch on the five dimensions on a scale of 1 to 5. He explains more on how he defined the rating in his blog. Let us assume that it is interval scale to run Multiple Linear Regression (OLS – Ordinary Least Squares).

Now, what are the problems with this predictive model?

1. How reliable is the data? Mr.Jamison collected 200 startup pitches available to him (not random sampling mind you) and ex post gave the rating. That is, these are NOT the ratings his firm gave on these dimensions at the time of the pitch but done by Mr.Jamison now just for the purpose of this analysis.
That is a biased sample with flawed measurement. You can stop right here and call him out. The rest of the article and his claims based on the regression analysis are point less.
2. How good is  the model? . A multiple regression model is measured by two metrics. One,  R2  which is the strength of the relation between the explanatory variables and the dependent variable and two a measure of whether each variable’s relation is statistically significant (p-value < 0.05)
This model has an R2 value of 0.5.  This means 50% of the changes in Liklihood  (the Right Hand Side variable) can be explained by changes in these four variables. But is each explanatory variable’s relation statistically significant? Mr. Jamison does not provide us t-stat (or p-value) data for us. This is likely because he simply ran the regression with all the variables and reported just the R2 .
If one were to use the simplistic Excel’s DataAnalysis tool to run multiple-regression that is what one will get. In essence, we do not know how many of the three variables really have any effect on the Likelihood of Receiving Term Sheet.
The right way to do the regression is to enter variables one at a time and see if its relation is statistically significant and if the R2 value changes with the addition of variable to the model. It is possible only one of the variable is relevant and its  Rcould be much lower than 50%.
So all the explanations on importance of Team, Market, Traction that Mr.Jamison provides are irrelevant because they are  based on faulty analysis.
3. About the use of term Likelihood: It is misleading as I first thought he was really measuring Likelihood using Logistic regression. It is OLS where he models Likelihood on a 5 point scale. That rating is quite meaningless: it is simply a binary variable, whether he extended term sheet or not. In which case he should be running Logistic Regression which measures the probability that a startup will get term sheet given the values of four explanatory variables.

Even if the model did not have any of these errors, there are still lurking variables. Regression is not causation despite the equation form. It is still correlation and there are many lurking variables including who introduced the startup for the pitch and whether the VCs identify themselves with the startup founders.

What this really means is VCs don’t have any real model for evaluating startups.  Consider this – if we took this raw data, stripped out the Likelihood variable and asked VCs (in general) to rate the likelihood, how different are these going to be from VC to VC and how different will these ratings be from one done based on coin-toss?

It would have been interesting if VCs had a scoring system for these four attributes and other dimensions,  as a team rated the startups right after the pitch and agreed to extend term sheet to only those that reached certain threshold.

But what we have here  is faulty data and analysis used to color gut calls as quantitative.

Are you going to willingly suspend your disbelief? Or …

## 5 thoughts on “Making up things and supporting with faulty analysis”

1. happy to provide all the raw data! :) i do have other stuff beyond the r-sq, just didn’t include in the post, as it ran a bit long. also, i don’t at all claim to be a stats expert, and would gladly engage with those who are, in order to improve the analysis. the extent it helps me make better decisions, i’m all ears. my attitude with this was that it was improtant to get something out, iterate and improve. seems that that’s the ethos of silicon valley founders and the lean startup methodology, so why shouldn’t i follow the same?

happy to continue engaging in discussion on how to improve. hit me up at jjamison brv com, if you’d like to .

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• Jay
Look forward to getting the data.
Thanks for sharing
-rags

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