ACP Classic and Sparkassen Chess Meeting - Round 1

The ACP Classic began with 3 draws and no adjournments. Nepomniachtchi became the slight favorite in the forecast. Since So drew a weaker player than he did, Nepo now has an easier schedule in the round robin.

Meanwhile in Dortmund... Last time it was tempting to write something like, "as usual, the tournament
features a few local players who have no chance of winning but give the host country someone to root for," with Meier and Baramidze in mind. Then Meier defeated Kramnik with the black pieces. The model remains skeptical of Meier's chances, but he is certainly worthy of participating in this event. Caruana's chances soared as a result of this game and his victory over Baramidze.

The standings:

Meanwhile in the University of Oregon Economics computer lab... Slow progress on refining the model's draw rate assumption. Simple models did not suffice, so more variables were added. This eventually lead to a technical issue called multicollinearity. An easy example illustrates the problem. Suppose that the true model is

Y = 2 X + random error

but we try to estimate "a" and "b" in

Y = aX + bX + random error

Since the truth is that Y = 2X, it must be the case that a + b = 2. But there is no unique solution for this equation; a = 2 and b = 0 works, but so does a = 1, b = 1, as does a = -14, b = 16, etc. For our project, I know for a fact that I'm not committing this exact error. But with many variables that are nearly identical, the computer can't tell the difference between perfect multicollinearity and almost perfect multicollinearity. As a result, it starts automatically dropping variables that are important, leaving me with estimates that may be flawed. Today I will start testing a possible solution to the problem. Ideally a new model will be ready in time for the Biel tournament that begins on Monday, but that seems unlikely.