Adrián Beltré, Todd Helton, and Joe Mauer have been elected to the Major Legaue Baseball Hall of Fame! I knew that Mauer would make the Hall of Fame eventually, but he far surpassed my prediction for how he was going to do this year. I am pleasantly surprised to see him make the Hall on the first ballot, as I thought that he would need to wait a year or two to be elected. Adrián Beltré obviously sailed into the Hall on the first ballot.
Billy Wagner, who was named on 73.8% of the ballots, missed election to the Hall by only 5 votes. He'll make it in 2025, which is his 10th and final year on the writers' ballot. Gary Sheffield was named on 63.9% of the ballots in his final year on the writers' ballot. His Hall case is now in the hands of the various small commitees, and I think (and hope) that he'll make it eventually. Andruw Jones had a small gain to 61.6% and Carlos Beltrán made a sizeable gain to 57.1%. Beltrán has an outside shot to be elected in 2025, but I think that 2026 is more likely. Andruw Jones could also ultimately make it through the writers' ballot, but I think that Beltrán will surpass Jones in the vote total in 2025. One way or another, they'll both eventually make the Hall of Fame. Chase Utley got 28.8% of the vote in his debut on the ballot. He did much better in the public ballots than in the private ones. I do think that Utley will eventually make it, but it's going to be a long road for the more sabermetrically-minded folks to convince others that Utley belongs in the Hall of Fame.
In December, a small committee elected former manager Jim Leyland to the Hall of Fame.
As usual, I have been following the ballot tracker very closely these past couple of months.
A discussion of a few ESPN.com writers of this year's biggest winners and biggest losers, as well as an outlook on the 2025 ballot.
Of the players who can debut on the writers' ballot in 2025, the only plausible Hall of Fame candidates are Ichiro Suzuki and C.C. Sabathia. Ichiro will sail into the Hall of Fame in his ballot debut (and hopefully he'll be elected unanimously, but I am not holding my breath). Sabathia will eventually make it, but I think that it's going to take a few years (say, 4 years).
Update (which I forgot to include in the original text of this post): My prediction for the 2025 balloting is that Ichiro Suzuki and Billy Wagner will be the two players elected. I think that Carlos Beltrán will get around 70% of the vote next year and that Andruw Jones will be in the mid 60s (perhaps around 66%). I think that Chase Utley will probably end up at about 35%. Utley's candidacy appears to be the latest battle in the considerations of old-school versus new-school voters.
Update: Jay Jaffe has written a rundown of the results of this year's writers' ballot.
Update (1/24/24): Here is Jay Jaffe's candidate-by-candidate dissection of this year's writers' ballot.
Update (1/29/24): Here is Jay Jaffe's five-year forecast of Hall of Fame balloting.
My name is Mason Porter. I am a Professor in the Department of Mathematics at UCLA. Previously I was Professor of Nonlinear and Complex Systems in the Mathematical Institute at University of Oxford. I was also a Tutorial Fellow of Somerville College.
Tuesday, January 23, 2024
Thursday, January 04, 2024
"Learning Low-Rank Latent Mesoscale Structures in Networks"
One of my papers came out in final form today. Here are some details.
Title: Learning Low-Rank Latent Mesoscale Structures in Networks
Authors: Hanbaek Lyu, Yacoub H. Kureh, Joshua Vendrow, and Mason A. Porter
Abstract: Researchers in many fields use networks to represent interactions between entities in complex systems. To study the large-scale behavior of complex systems, it is useful to examine mesoscale structures in networks as building blocks that influence such behavior. In this paper, we present an approach to describe low-rank mesoscale structures in networks. We find that many real-world networks possess a small set of latent motifs that effectively approximate most subgraphs at a fixed mesoscale. Such low-rank mesoscale structures allow one to reconstruct networks by approximating subgraphs of a network using combinations of latent motifs. Employing subgraph sampling and nonnegative matrix factorization enables the discovery of these latent motifs. The ability to encode and reconstruct networks using a small set of latent motifs has many applications in network analysis, including network comparison, network denoising, and edge inference.
Title: Learning Low-Rank Latent Mesoscale Structures in Networks
Authors: Hanbaek Lyu, Yacoub H. Kureh, Joshua Vendrow, and Mason A. Porter
Abstract: Researchers in many fields use networks to represent interactions between entities in complex systems. To study the large-scale behavior of complex systems, it is useful to examine mesoscale structures in networks as building blocks that influence such behavior. In this paper, we present an approach to describe low-rank mesoscale structures in networks. We find that many real-world networks possess a small set of latent motifs that effectively approximate most subgraphs at a fixed mesoscale. Such low-rank mesoscale structures allow one to reconstruct networks by approximating subgraphs of a network using combinations of latent motifs. Employing subgraph sampling and nonnegative matrix factorization enables the discovery of these latent motifs. The ability to encode and reconstruct networks using a small set of latent motifs has many applications in network analysis, including network comparison, network denoising, and edge inference.