I'm generally pretty skeptical of the value data-based election models to begin with, particularly the way that its used in 99.9% of instances. I think what's true of elections and I think this is true of sports as well is that this data is very good as an explanatory tool after the event, I'm pretty skeptical of its value as a predictive tool before the event.
I'm generally pretty skeptical of the value data-based election models to begin with, particularly the way that its used in 99.9% of instances. I think what's true of elections and I think this is true of sports as well is that this data is very good as an explanatory tool after the event, I'm pretty skeptical of its value as a predictive tool before the event.
It's worth noting that Silver's first big success was in creating a predictive tool (PECOTA), which was pretty good, though of course such tools can only be so good as there's just too much data that simply can't be input into an algorithmic model.
I think he got pundit brain because he knows the model can only do so much. On top of that, I think the model is just inputting garbage now (because polls are garbage, just universally).
Believe me, I'm aware of Silver's background. But the reason I've been skeptical of him pretty much since day 1 is that I think if you asked baseball scouts, managers, GM's and asked them to predict a player's performance, you'd get just as accurate of information
I get where you're coming from, but in reality we're all relying on data for all of our predictions. What we lack is a thorough and consistent process for applying that data.
Having a model is a way to formalize that data process and make it consistent. We will never see a perfectly predictive model, and that's OK. We shouldn't expect one. Just like we shouldn't expect it from more traditional pundits.
I think we all know that tossup/lean/likely/safe all have degrees within them. Sabato has WI, MI, AZ, NV, PA, GA, and NC all as tossup states for the presidential election. That doesn't truly mean each of them is exactly as likely to go to Harris, but more that they exist in some spectrum of maybe something in the range of 45-55 to 55-45. Similar idea for lean and likely. There's little practical difference between "Lean D" and "70.3% of D win" in that sense. They're both the result of models, one informal and one formal. The exactness of one prediction is a result of it being a formal model and the consequences of it being mathematically based, rather than it having anything approaching that degree of confidence.
So long as we take into account the limitations at play I rather like formal data models. If a prediction changes, it will be known an obvious why. If you feed it the same exact data to two different elections, it will give the same prediction. There's no fretting about emotions and secret sources and personal bias. There's a place for them if they can source good data.
I've heard this before, I just haven't seen any reason to believe that these models are any more accurate than simply asking the people who would know like Sabato and asking them to put percentages on a candidate's likelihood to win
I'm generally pretty skeptical of the value data-based election models to begin with, particularly the way that its used in 99.9% of instances. I think what's true of elections and I think this is true of sports as well is that this data is very good as an explanatory tool after the event, I'm pretty skeptical of its value as a predictive tool before the event.
It's worth noting that Silver's first big success was in creating a predictive tool (PECOTA), which was pretty good, though of course such tools can only be so good as there's just too much data that simply can't be input into an algorithmic model.
I think he got pundit brain because he knows the model can only do so much. On top of that, I think the model is just inputting garbage now (because polls are garbage, just universally).
Believe me, I'm aware of Silver's background. But the reason I've been skeptical of him pretty much since day 1 is that I think if you asked baseball scouts, managers, GM's and asked them to predict a player's performance, you'd get just as accurate of information
I get where you're coming from, but in reality we're all relying on data for all of our predictions. What we lack is a thorough and consistent process for applying that data.
Having a model is a way to formalize that data process and make it consistent. We will never see a perfectly predictive model, and that's OK. We shouldn't expect one. Just like we shouldn't expect it from more traditional pundits.
I think we all know that tossup/lean/likely/safe all have degrees within them. Sabato has WI, MI, AZ, NV, PA, GA, and NC all as tossup states for the presidential election. That doesn't truly mean each of them is exactly as likely to go to Harris, but more that they exist in some spectrum of maybe something in the range of 45-55 to 55-45. Similar idea for lean and likely. There's little practical difference between "Lean D" and "70.3% of D win" in that sense. They're both the result of models, one informal and one formal. The exactness of one prediction is a result of it being a formal model and the consequences of it being mathematically based, rather than it having anything approaching that degree of confidence.
So long as we take into account the limitations at play I rather like formal data models. If a prediction changes, it will be known an obvious why. If you feed it the same exact data to two different elections, it will give the same prediction. There's no fretting about emotions and secret sources and personal bias. There's a place for them if they can source good data.
I've heard this before, I just haven't seen any reason to believe that these models are any more accurate than simply asking the people who would know like Sabato and asking them to put percentages on a candidate's likelihood to win