Read the race as it changes

Generated from content/lms/racecraft-and-strategy/04-race-strategy/01-reading-the-race.md; edit the source file, not this page.

Source path: content/lms/racecraft-and-strategy/04-race-strategy/01-reading-the-race.md

Course: Racecraft & Strategy

Module: Race Strategy

Estimated duration: 55 minutes

Principle

A race is not a fixed plan. It is a moving information problem. You may start with a strategy, a preferred pace, a target car, and an idea of where the track gives you opportunity, but the race you actually drive keeps changing. The surface has its own personality, each run has its own pattern, your car gives you new information, the drivers around you make decisions, and the useful plan is the one that keeps updating without becoming noisy. The skill in this lesson is the update loop: build a baseline, notice what changed, decide why it changed, choose a response simple enough to execute, and verify afterward whether the response was real speed or only a story that felt convincing in the car.

This lesson sits between the sibling skills in this module. Preloading a decision before pressure arrives only works when your preload matches the situation that actually appears. Building the pass two corners ahead only works when you keep noticing whether the pass is still forming or has already disappeared. Turning fast laps into racecraft only works when you can separate the lap you hoped to drive from the race that is unfolding in front of you. Reading the race is the sense-making layer under those decisions.

The clean rule is this: keep a live model of the race, but keep it simple enough that you can drive well while updating it. That sounds easy until you try to do it at speed. The car is feeding you steering, brake, throttle, speed, and grip information. The track is giving you surface and layout information. Your own consistency is changing from lap to lap. The data logger, if you have one, can record what the car and driver are doing, but it cannot make the decision for you in the moment. Your job is to train the loop so the important change rises above the noise.

Mechanism

Ross Bentley frames one of the big separators between great drivers and average drivers as the ability to receive and process dynamic information. The point is not that a great driver consciously thinks more sentences while driving. The point is almost the opposite. The body gathers huge amounts of information, the brain processes it very quickly, and the driver executes through trained movement. The more you practice receiving dynamic information, the more sensitive you become to it. Much of that useful processing happens below the fully conscious level, which means your conscious mind should not be overloaded with ten competing observations.

That matters for race strategy because a changing race rarely announces itself with one clean label. You usually get fragments. A car ahead starts missing the same apex. Your own minimum speed changes in one section. A brake trace after the session shows that one lap was better in one phase even though it was not the fastest lap. A track that looked familiar on the map feels different once you are on it. The driver who reads the race well does not need a dramatic event before updating. They recognize small deltas early, then keep the next decision small.

Bentley also reminds drivers that every racetrack has its own personality, and that even tracks that look similar can feel different. That is the track version of the same principle. Do not treat the layout, the plan, or the previous session as the whole truth. The permanent road course, the temporary circuit, the airport course, and the oval all ask you to adapt. Even if two places appear similar on paper, the useful reading comes from the current feel and the current evidence.

The data sources reinforce the same discipline after the car stops. Logged data is valuable because it records what the vehicle and driver were doing, and comparing different laps or runs can reveal driver performance or setup effects. Metrics and run charts can accelerate interpretation and decision making when the data set is large. But the useful driver habit is not to worship every squiggle. The recurring advice is to keep learning, keep it simple, focus on the basics, and ask why. That is exactly the attitude you need in the car.

The race-reading loop

Use five steps. First, establish the baseline. Second, scan for deltas. Third, sort the likely cause. Fourth, choose one executable response. Fifth, review afterward with evidence. If you skip the baseline, every sensation feels like a surprise. If you skip the delta scan, you keep driving the old race after the race has changed. If you skip cause sorting, you react to symptoms. If you choose a response that is too complicated, you stop driving cleanly. If you skip review, you do not know whether your read was accurate.

Step one is the baseline. Early in the stint, you need a simple picture of normal. Normal is not the theoretical perfect lap. Normal is the current operating picture: what the track feels like, where your car is stable, where you are giving away speed, where the group around you is strong or weak, and what your own repeatable rhythm looks like. This is not a request to drive slowly or passively. It is a request to know what you are comparing against. Data analysis works by comparing laps, runs, and patterns; in-car race reading works the same way. Without a reference, a change is just noise.

For an intermediate driver, the baseline should be small enough to remember under pressure. Use three channels: track, car, and race. Track means the surface and layout as you are experiencing them now. Car means your own vehicle response and your own input quality. Race means the movement of the cars around you, the gaps, and the decisions that may be forming. If you try to monitor twenty things, you will lose the car. If you monitor only lap time, you will miss the information that explains the lap time.

Step two is the delta scan. Each lap, ask what is different from the baseline. The word different matters. You are not trying to describe everything. You are trying to notice change. Did the same section get easier or harder to repeat. Did your throttle timing change without you intending it. Did a car ahead become predictable in one place and erratic in another. Did your brake pressure or release pattern feel different. Did the track ask for a different entry commitment than the previous session. The scan is useful only when it is tied to comparison.

This is where the fastest-lap trap catches many drivers. The bonded data material gives a clear warning: if you look only at the fastest lap, you can miss important information. One lap may have the best version of one section while another lap has the best version of a different section. The race-reading lesson is the same. Do not let one headline number become your whole truth. A lap that is not your fastest can still contain the clue that explains the race. A car you are not yet passing can still show you where the race is moving. A small change in your input trace can explain why a plan started working or stopped working.

Step three is cause sorting. Once you notice a delta, do not jump straight to a tactic. Ask why. The source material on data analysis keeps returning to that question because raw information is not yet understanding. In the car, cause sorting has to stay simple. Put the change into one of a few buckets. Is this a track change, a driver-input change, a vehicle or tire behavior change, or a race-position change. You will not always know perfectly in the moment, but the act of sorting prevents the worst mistake: treating every problem as an aggression problem or every improvement as proof that you should push harder everywhere.

If the same corner suddenly feels slower because your own braking was earlier and softer, the response is not the same as if the car is genuinely losing response. If the driver ahead is weak in one section but strong on exit, the response is not the same as if they are inconsistent everywhere. If your data later shows that your best throttle application and best brake release lived on different laps, the response is not to praise the fastest lap and move on. The response is to understand which pieces belong together.

Step four is the executable response. At race pace, a good response is small, concrete, and reversible. Small means you are changing one emphasis, not rewriting the whole lap. Concrete means you can state what you will do on the next lap. Reversible means that if the read is wrong, you can return to baseline without needing a long recovery. This aligns with the data-side advice to keep the work simple and focus on the basics. Race reading is not valuable because it produces clever thoughts. It is valuable because it produces the next clean action.

A clean response might be to stop chasing one car in a section where the data and feel show you are overdriving. It might be to delay a pass attempt because the pattern ahead no longer supports it. It might be to protect exit speed in a section where the race keeps compressing. It might be to return to your baseline rhythm because the change you noticed was your own excitement rather than a real opportunity. The details of passing and pressure are taught in the sibling lessons; here the skill is recognizing whether the situation still supports the decision.

Step five is the review. After the session, your memory is not enough. Logged data can show what the vehicle and driver did, and comparative analysis can reveal differences between laps and runs. Use that review to test the reads you made in the car. Did the section you thought was improving actually improve. Did your throttle or brake trace support your story. Did the fastest lap hide a weaker pattern that will hurt you later. Did a setup or vehicle behavior change show up in a way you can understand. The goal is not to become a data engineer before the next race. The goal is to close the loop so your next in-car reads are better.

Sub-skill: building a useful baseline

A useful baseline has to be felt, simple, and reviewable. Felt means you can recognize it while driving. Simple means it fits inside your attention. Reviewable means you can compare it after the run using whatever evidence you have, from notes to lap comparisons to speed, throttle, and brake traces. If your baseline is only a lap-time goal, it is too thin. If your baseline is a full engineering model of the car, it is too heavy. Use a driver-sized baseline.

Start with track personality. The same layout category does not guarantee the same feel. A permanent road racing course, a temporary circuit, an airport course, and an oval all ask for different adaptation. Even two tracks that appear similar can feel different. At the start of a race or session, remind yourself that the map is not the race. Your first job is to connect the known plan to the current track feel.

Then add your car baseline. You need to know what normal response feels like before you can notice meaningful change. This includes how the car accepts brake release, how it responds at turn-in, whether your throttle application is clean, and whether the same section is repeatable. The chassis and suspension material in the corpus emphasizes that understanding vehicle behavior and adjustments is part of the driver's job. You do not need to become the engineer in the cockpit, but you do need enough understanding to avoid blaming the wrong thing.

Finally add the race baseline. You are not only driving a lap. You are driving among other changing objects and decisions. For this lesson, keep that read plain. Who is stable. Who is varying. Where does the group compress. Where does the group stretch. Where are you tempted to drive differently because of another car rather than because the opportunity is real. Do not turn this into a pass plan yet. You are building the picture that the pass plan will use.

Sub-skill: noticing deltas without chasing noise

A delta is a difference that matters. Not every sensation is a delta. Not every lap-time movement means the race has changed. The data chunks are useful here because they warn against overvaluing one number. A fastest lap can hide important information, and comparing different laps or runs reveals patterns. The in-car equivalent is to look for repeated or explained change, not isolated emotion.

Use repetition as your first filter. If the same change appears in the same place more than once, it deserves attention. If a car ahead misses one apex one time, you have a note. If it happens in a pattern, you may have a race-read. If your own throttle feels hesitant once, it may be traffic, timing, or distraction. If the hesitation repeats in the same phase, you have a driver-input question to review.

Use connection as your second filter. A useful delta connects to a cause or decision. If the change has no possible action, park it until the debrief. If it can shape the next lap, keep it. This protects you from information overload. The sources on data analysis point toward extracting and interpreting information efficiently. The driver version is to keep only the deltas that can change the next clean action.

Use humility as your third filter. Race reading is probabilistic. You can be wrong. That is why the response needs to be small and reviewable. When you notice a change, treat it as a hypothesis. The next lap tests it. The data after the session tests it again. This is how you improve without pretending that your first interpretation was perfect.

Sub-skill: sorting cause before response

Cause sorting is where intermediate drivers often make the leap too early. You feel a change and immediately decide what it means. The car feels slower, so you assume the tires are gone. The lap time drops, so you assume you drove better. A competitor gets closer, so you assume pressure is the problem. The corpus gives you a better model: analyze driver performance, vehicle behavior, tire performance, and track data as related but distinct areas. In the car, that becomes a simple sorting question before you act.

Driver cause means your input changed. You braked differently, turned differently, released differently, or used throttle differently. Vehicle or tire cause means the car's response changed in a way that is not explained only by your intended input. Track cause means the same plan meets a different current surface or layout demand. Race cause means the cars around you changed the timing, space, or pressure. Often more than one bucket is involved, but naming the first likely cause keeps your response from becoming random.

This matters because the same symptom can require opposite actions. A slower section may need calmer input, not more effort. A stronger exit may be the result of a better entry, not proof that you should attack the next braking zone harder. A group compressing ahead may create opportunity, or it may remove the space you planned to use. Reading the race means holding the symptom long enough to ask why before you spend the car, the position, or the lap.

Sub-skill: keeping the response executable

The best race read is useless if the response is too large to drive. Keep it in a one-lap instruction. Examples of the right size are: return to baseline brake timing here, protect the exit here, observe the car ahead one more lap here, stop comparing only the fastest lap in debrief, or check whether the same section repeats in the trace. Those are small enough to execute and review. Examples of the wrong size are: drive smarter, be more aggressive, find speed everywhere, or figure out the car. Those are slogans, not actions.

The reason this works is the same reason data analysis benefits from metrics and run charts. Large data sets need interpretation tools that reduce complexity. A driver at speed needs the same reduction. Your race read should become one next action, not a full essay in your helmet. After the session, you can expand the analysis. In the car, you compress it.

A good response also respects the boundary between this lesson and the tactical lessons around it. Reading that the race has changed does not automatically mean pass now. It may mean the pass you were building is no longer there. It may mean the next two corners are becoming the setup phase. It may mean your preloaded decision is now valid. It may mean your best racecraft is to stabilize the lap and keep learning. The read informs the tactic; it is not the tactic by itself.

Sub-skill: using data without becoming data-bound

Data is powerful because it records the vehicle and driver, and because comparing laps or runs can reveal what changed. The bonded material also makes the practical point that accessible logging gives many teams similar information, so the edge goes to the team or driver who uses the information more efficiently. For a club racer or HPDE driver, that means the advantage is not owning the most complicated screen. The advantage is asking better questions of the evidence you already have.

Start with the simplest post-session comparison. Do not open the data only to admire the fastest lap. Pick two laps that matter: the lap that felt best and the lap that produced the best number, or the lap before and after you made a deliberate change. Compare the same section. Look at speed, throttle, and brake if you have them. If you do not have channels, use lap notes and sector times if available. The question is not which lap wins overall. The question is what each lap teaches about the changing race.

Then ask why. If the faster lap is faster only because one section was excellent while another section was weak, your next plan should combine the best repeatable pieces rather than worship the full lap. If your throttle trace shows hesitation in a section where you thought you were decisive, that changes your baseline. If a brake trace shows that your in-car read of being later was actually only being softer or less repeatable, that changes your cause sorting. Data does not replace the in-car read. It calibrates it.

Be careful with data quality. One of the data acquisition chunks states the basic requirement: usable data has to be measured correctly. If a channel is not trustworthy, do not build a big conclusion from it. Use the channels you understand, cross-check with feel, and keep the lesson tied to decisions you can actually test next time. The goal is to improve your race reading, not to create a complicated explanation from weak evidence.

Calibration cues

You are improving when surprises get earlier. The race will still change, but you will notice the change before it becomes a panic. You will hear yourself making smaller, clearer decisions. You will stop saying only that the car felt off and start naming whether the likely issue was driver input, vehicle or tire response, track personality, or race-position pressure. You will still be wrong sometimes, but your wrong reads will become easier to review because they were specific.

You are improving when your debrief changes shape. Instead of reporting only lap time, you can explain which lap or section contained the useful information. You can say which part of the fastest lap was not actually your best work. You can compare two laps or runs and identify a pattern. This matches the data lesson that looking only at the fastest lap can miss important information. A mature debrief treats the fastest lap as one data point, not the whole story.

You are improving when the in-car loop stays calm under pressure. Intermediate drivers often get louder internally when the race changes. A better race reader gets simpler. The scan becomes track, car, race. The question becomes what changed and why. The response becomes one action for the next lap. That calm is not passivity. It is the mental space that lets your trained driving continue while your strategy updates.

You are improving when your post-session data and your in-car notes start agreeing more often. Perfect agreement is not required. In fact, disagreement is useful because it shows where your perception needs calibration. If you thought you were earlier to throttle but the trace shows hesitation, that is a training gift. If you thought the car changed but the data suggests your input changed first, that is also useful. The point is to keep learning from the loop.

Failure modes

The first failure mode is fastest-lap tunnel vision. You decide that the fastest lap contains the best version of the race, so you stop investigating. The cost is that you miss the usable pieces from other laps. You may repeat the wrong behavior because the headline number rewarded it once. Recovery is simple: compare laps or runs by section and by phase, not only by total time.

The second failure mode is noise addiction. You notice everything and understand nothing. The cost is attention. Your driving gets worse because your conscious mind is trying to process more than it can use. Recovery is to return to the three-channel scan: track, car, race. If a piece of information cannot change the next clean action or the next debrief question, let it go for now.

The third failure mode is static-plan loyalty. You made a plan before the race, so you keep executing it after the race has changed. The cost is missed opportunity or unnecessary risk, depending on the situation. Recovery is to treat the plan as a hypothesis. The baseline tells you what you expected; the delta tells you whether the expectation still matches reality.

The fourth failure mode is instant blame. The car feels different, so you blame the car. The other driver changes pace, so you blame traffic. The lap time changes, so you blame conditions. Sometimes those explanations are right, but the driver has to know enough about vehicle behavior, track personality, and input quality to ask why before settling on the answer. Recovery is cause sorting: driver, vehicle or tire, track, race.

The fifth failure mode is book-only confidence. Bentley points out that driving a race car is not something you can do only from a book, even though understanding theory helps you learn faster once you are behind the wheel. Reading the race is exactly that kind of skill. The lesson gives you the structure, but the sensitivity comes from hands-on practice, review, and repetition. Recovery is to turn the lesson into a drill at the next event, not a belief you carry around.

The sixth failure mode is data-as-scoreboard. You open the logger only to prove that you were fast or slow. The cost is lost learning. The same logged information can be used to measure performance and give you an advantage next time the vehicle hits the track, but only if you use it to ask why. Recovery is to choose one question before opening the data. For example: did the section where I thought the race changed actually change in speed, throttle, or brake behavior.

How to practice this at intermediate level

Do not try to become a race strategist by adding more thoughts. Practice by making the loop more repeatable. Before a session, write one baseline expectation for track, car, and race. During the session, scan only for deltas against those three items. After the session, compare one or two laps or runs that test your best in-car read. The practice is small by design. Sensitivity improves through repeated dynamic information processing, not through one heroic analysis.

Keep the scope honest. If the bonded data does not show enough to support a conclusion, stop short of the conclusion. If your in-car read was vague, make the next read more specific. If your data is not measured correctly or a channel is not trustworthy, do not overbuild the interpretation. A disciplined driver would rather carry one true lesson into the next run than five impressive guesses.

The standard for this lesson is not that you always make the perfect call. The standard is that your race does not happen to you as a series of surprises. You maintain a simple live model, you update it when evidence changes, you make one clean response, and you review the response afterward. That is how the race becomes readable.

Worked example: red and blue laps on the same section

The data-for-drivers chunk gives a useful race-reading example even though it comes from post-session analysis. The lesson on the slide is that if you looked only at the fastest lap, you would miss important information, and that putting the useful pieces of two laps together could reveal more speed. The displayed channels include GPS speed, throttle position, and front brake pressure. Treat that as a model for how to read a changing race.

Imagine you came in saying that lap red was the race because it was the quicker lap. Lap blue felt less impressive, so you were ready to ignore it. The better question is not which lap wins as a whole. The better question is what each lap proves in the same section. One lap may show cleaner brake timing. Another may show a better throttle pickup. One may have a better entry but a weaker exit. If you only crown the total lap, you may throw away the part of the race that is trying to teach you something.

Now move that lesson back into the car. When the race changes, do not wait for the lap time to label it. If a section starts feeling easier to repeat, mark that as a possible delta. If another section feels faster but messier, do not assume it is better. After the session, compare the relevant laps. The success is not simply finding the quicker trace. The success is matching your in-car read to an evidence-backed explanation of what changed and why.

Worked example: the same layout, different personality

Bentley notes that every racetrack has its own personality and that even seemingly identical layouts can feel different. Use that as a race-strategy example. You arrive with a mental plan based on a map, a previous event, or a track that looks similar. That plan is useful, but it is not the race. The race begins when the current place starts giving you information.

On a permanent road course, you may settle into a baseline quickly because the references feel stable. On a temporary circuit or airport course, the same driver may need more deliberate sensitivity because the surface and visual environment can feel less familiar. The lesson is not that one type is always easier or harder. The lesson is that the plan must pass through the current track personality before you trust it.

Your in-car loop is the same. Establish what normal feels like today. Scan for what changes. Sort whether the change comes from the track, the car, your input, or the race around you. Choose one response. After the session, compare laps or runs rather than relying on memory alone. That is how adaptation becomes a method instead of a mood.

Worked example: carrying the loop across different car classes

The Going Faster material names a range from Formula Dodge and Showroom Stock to Indy Cars as examples of specific race cars whose handling, tire choices, chassis adjustments, and driving modifications matter. The bonded chunk does not give setup detail for those cars, so do not invent it. The supported lesson is narrower and still important: the race-reading loop has to survive a change in car context.

A driver moving between a school formula car, a showroom-based car, and a higher-performance car should not expect identical sensations or identical timing. But the structure of the read can remain the same. What is the baseline for this car. What changed from that baseline. Is the change driver input, vehicle or tire response, track personality, or race-position pressure. What one response can be tested on the next lap. What evidence after the run supports or corrects the read.

This matters because intermediate drivers often carry confidence from one car into another without rebuilding the baseline. The safer and faster habit is to keep the information loop constant while letting the details of the car teach you. The car class changes the feel. It does not remove the need to compare, ask why, and review.

Common mistakes

Mistake one is reading the lap time instead of the race. Good looks like using lap time as one clue among several, then comparing sections, inputs, and repeatability.

Mistake two is treating the fastest lap as the best lap everywhere. Good looks like asking which parts of different laps should be combined, because the fastest total lap may not contain the best version of every section.

Mistake three is reacting before sorting cause. Good looks like pausing long enough to name the likely bucket: driver input, vehicle or tire behavior, track personality, or race-position pressure.

Mistake four is collecting too much information. Good looks like keeping the scan to track, car, and race while driving, then expanding the analysis after the session.

Mistake five is blaming the car before understanding the car. Good looks like knowing enough about chassis and suspension behavior to ask an informed question, while still checking your own inputs first.

Mistake six is using data only after disappointment. Good looks like using data every time as a learning loop: compare, interpret, ask why, and carry one clear adjustment into the next run.

Mistake seven is assuming the plan survives unchanged. Good looks like treating the plan as a starting hypothesis that must be updated when the current track, current car behavior, or current race pattern changes.

Drill: the three-channel change log

Run this drill over three sessions at your next event or race weekend. The count is three sessions. The duration is the full session plus ten minutes immediately afterward. The success criterion is that you can name one real delta from each channel, test one response, and later compare the read against notes or data.

Before session one, write three baseline expectations: one for track, one for car, and one for race. Keep each to one sentence. During the session, do not add more categories. Each lap, ask only what changed from those three baselines. After the session, write the three deltas you actually noticed. If you noticed none, write that. Honesty is better than decoration.

Before session two, choose one delta from session one and make it testable. For example, if you thought one section changed because your input changed, plan one small input emphasis. If you thought a race-position pattern changed, plan one observation point. During the session, execute only that test while continuing the three-channel scan. After the session, compare the relevant laps or runs if you have data. If you have speed, throttle, and brake, use them. If you do not, use lap notes and timing fragments.

Before session three, refine the loop. Drop any observation that did not change a decision. Keep the observation that improved your next action. The goal by the end is not a perfect strategy notebook. The goal is a repeatable process: baseline, delta, cause, response, review. If the drill leaves you calmer and more specific under pressure, it is working.

When this principle breaks down

The principle breaks down when you pretend weak information is strong. If a data channel is not measured correctly, do not treat it as proof. If your memory of the session is vague, do not build a detailed theory from it. If the bonded evidence or your own evidence supports only a small conclusion, stop at the small conclusion.

It also breaks down when the in-car model becomes too large. Dynamic information processing improves with practice, but that does not mean your conscious mind should try to hold the entire data set while driving. If you feel overloaded, reduce the scan. Track, car, race. What changed. Why might it have changed. What is the next clean action.

Finally, it breaks down when you use reading as a substitute for driving. The purpose of the read is better execution. Bentley's introduction makes clear that theory can help you learn more quickly once you are behind the wheel, but driving is learned through hands-on experience. So use the structure, then practice it in the car, review it with evidence, and bring one sharper read into the next session.

Author Review

No quiz questions are attached to this lesson.

Sources

#	Document	Chunk	Pages	Score	Collection
1	Inner Speed Secrets - Ross Bentley	289367de-9265-b536-97d3-635fbf66897e	28	1	uio_books_raw_v1
2	Ultimate Speed Secrets - Ross Bentley	47f6de8d-9d56-5b6d-547a-f1e7bb92faaf	152	1	uio_books_raw_v1
3	Data-for-Drivers-PRINT	c493f39d-9ba1-5829-3168-d38e471cc061	9	1	uio_books_raw_v1
4	Analysis Techniques for Racecar Data Acquisition	ad559d04-3651-61c2-d02b-5455aba0d7cc	7	1	uio_books_raw_v1
5	Analysis Techniques for Racecar Data Acquisition	5eeea298-6191-0fb2-1054-b10fe574a804	2	1	uio_books_raw_v1
6	Analysis Techniques for Racecar Data Acquisition	d0db9128-dc9a-aec3-14a8-5f101654753f	3	1	uio_books_raw_v1
7	Data-for-Drivers-PRINT	cb13d8c3-cc6b-28e9-246f-c3c64ae01efc	1	1	uio_books_raw_v1
8	Analysis Techniques for Racecar Data Acquisition	1d32f116-9b81-52c6-919d-dba1c542c011	5	1	uio_books_raw_v1
9	Analysis Techniques for Racecar Data Acquisition	9ee3c928-9190-4c5d-2314-158932244c31	22	1	uio_books_raw_v1
10	Ultimate Speed Secrets - Ross Bentley	0237a5bd-e2d4-724e-bc2e-ba13db924f66	11	1	uio_books_raw_v1
11	Going Faster Mastering the Art of Race Driving - Carl Lopez	fa01ec16-aace-9079-2afa-de127b8272a9	300	1	uio_books_raw_v1
12	Ultimate Speed Secrets - Ross Bentley	149c4d5c-d228-0358-acc0-8a92ac07ec7c	50	1	uio_books_raw_v1